Find preprocess workflow here.
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
## ✓ ggplot2 3.3.2 ✓ purrr 0.3.4
## ✓ tibble 3.0.4 ✓ dplyr 1.0.2
## ✓ tidyr 1.1.2 ✓ stringr 1.4.0
## ✓ readr 1.4.0 ✓ forcats 0.5.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
##
## Attaching package: 'psych'
## The following objects are masked from 'package:ggplot2':
##
## %+%, alpha
## This is lavaan 0.6-8
## lavaan is FREE software! Please report any bugs.
##
## Attaching package: 'lavaan'
## The following object is masked from 'package:psych':
##
## cor2cov
##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## .default = col_double(),
## id = col_character(),
## oth_text = col_character(),
## expoth_text = col_character(),
## sex = col_character(),
## edulvl1 = col_character(),
## spec1 = col_character(),
## edulvl2 = col_character(),
## spec2 = col_character(),
## jobfield = col_character(),
## jobpos = col_character(),
## city = col_character()
## )
## ℹ Use `spec()` for the full column specifications.
## tibble [495 × 133] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ id : chr [1:495] "00XzIUUVmQ" "0aABrq9MBY" "0c6myGTrKr" "0CS5iaAVos" ...
## $ e_dighelp : num [1:495] 5 NA 4 3.33 3 ...
## $ n_dighelp : num [1:495] 1 NA 1 3 2 1 3 2 4 1 ...
## $ e_socnet : num [1:495] 0 NA 3.2 3.6 4.5 ...
## $ f_socnet : num [1:495] 3 NA 2.4 1.6 2 ...
## $ n_socnet : num [1:495] 2 NA 5 5 2 2 2 4 3 4 ...
## $ gt_score : num [1:495] 2.67 2.67 2.83 2.5 2.67 ...
## $ pr01 : num [1:495] 3 3 3 3 3 4 3 1 3 4 ...
## $ pr02 : num [1:495] 3 3 3 3 3 3 3 1 3 3 ...
## $ pr03 : num [1:495] 3 3 3 3 3 1 5 1 3 4 ...
## $ pr04 : num [1:495] 2 3 3 3 2 0 3 2 4 3 ...
## $ pr05 : num [1:495] 3 2 1 2 3 4 4 1 0 3 ...
## $ pr06 : num [1:495] 2 2 4 3 3 5 4 3 4 4 ...
## $ pr07 : num [1:495] 3 3 3 3 3 5 3 2 1 3 ...
## $ pr08 : num [1:495] 2 3 4 2 3 4 4 4 3 4 ...
## $ pr09 : num [1:495] 2 4 3 3 4 4 4 3 3 4 ...
## $ pr10 : num [1:495] 2 3 3 2 3 4 3 3 1 3 ...
## $ co01 : num [1:495] 2 3 3 2 3 4 3 2 4 3 ...
## $ co02 : num [1:495] 3 3 3 2 3 3 3 1 2 3 ...
## $ co03 : num [1:495] 3 4 4 2 3 4 4 2 2 3 ...
## $ co04 : num [1:495] 4 2 4 4 3 5 4 3 5 4 ...
## $ co05 : num [1:495] 2 2 3 2 3 4 4 2 3 2 ...
## $ co06 : num [1:495] 3 3 4 4 3 3 4 2 2 3 ...
## $ co07 : num [1:495] 2 3 1 1 1 1 1 3 0 1 ...
## $ co08 : num [1:495] 2 2 2 1 4 5 2 1 2 1 ...
## $ co09 : num [1:495] 2 3 2 1 4 4 3 2 1 2 ...
## $ co10 : num [1:495] 3 2 3 2 3 5 3 1 1 2 ...
## $ ut01 : num [1:495] 3 4 4 5 4 5 5 4 4 3 ...
## $ ut02 : num [1:495] 2 3 3 4 4 5 5 3 3 3 ...
## $ ut03 : num [1:495] 3 2 4 5 1 5 3 4 3 3 ...
## $ ut04 : num [1:495] 3 3 3 4 4 5 4 3 2 3 ...
## $ ut05 : num [1:495] 3 2 3 5 4 3 4 3 4 3 ...
## $ ut06 : num [1:495] 2 3 4 4 4 5 4 3 3 3 ...
## $ ut07 : num [1:495] 3 2 4 5 4 4 4 3 4 3 ...
## $ ut08 : num [1:495] 3 3 3 4 4 5 4 3 4 4 ...
## $ ut09 : num [1:495] 2 3 3 3 3 4 4 3 3 4 ...
## $ ut10 : num [1:495] 2 2 2 4 1 2 2 1 3 3 ...
## $ ut11 : num [1:495] 2 2 3 4 3 2 4 1 1 3 ...
## $ ut12 : num [1:495] 3 4 4 3 4 2 4 3 3 4 ...
## $ fa01 : num [1:495] 2 2 3 4 2 2 3 3 2 2 ...
## $ fa02 : num [1:495] 2 3 2 4 2 0 2 3 2 1 ...
## $ fa03 : num [1:495] 3 3 3 1 4 2 1 0 3 2 ...
## $ fa04 : num [1:495] 2 3 3 2 1 2 2 0 1 2 ...
## $ fa05 : num [1:495] 3 3 3 3 4 4 3 3 2 3 ...
## $ fa06 : num [1:495] 3 2 4 3 3 2 4 0 2 3 ...
## $ fa07 : num [1:495] 3 3 3 3 1 3 2 1 1 3 ...
## $ fa08 : num [1:495] 3 2 2 2 1 2 2 3 2 2 ...
## $ fa09 : num [1:495] 3 2 3 4 2 1 2 3 2 2 ...
## $ fa10 : num [1:495] 2 2 2 4 1 3 3 4 4 2 ...
## $ de01 : num [1:495] 3 2 3 3 3 3 4 3 3 3 ...
## $ de02 : num [1:495] 3 3 3 3 3 4 4 2 1 3 ...
## $ de03 : num [1:495] 3 3 4 3 3 5 3 2 1 3 ...
## $ de04 : num [1:495] 2 3 2 0 2 2 0 3 1 3 ...
## $ de05 : num [1:495] 3 3 4 3 3 5 5 4 4 4 ...
## $ de06 : num [1:495] 2 3 3 3 3 3 4 0 0 3 ...
## $ de07 : num [1:495] 3 2 3 3 3 3 4 3 3 3 ...
## $ de08 : num [1:495] 3 3 3 3 3 5 4 1 1 3 ...
## $ de09 : num [1:495] 4 2 3 3 3 5 5 4 1 4 ...
## $ de10 : num [1:495] 3 3 4 3 3 3 4 3 3 3 ...
## $ de11 : num [1:495] 2 3 2 3 2 1 4 4 1 1 ...
## $ un01 : num [1:495] 3 3 3 2 4 3 4 3 4 4 ...
## $ un02 : num [1:495] 2 2 3 2 4 3 4 2 3 3 ...
## $ un03 : num [1:495] 3 1 3 3 4 5 4 2 4 3 ...
## $ un04 : num [1:495] 3 1 3 3 4 3 3 2 4 3 ...
## $ un05 : num [1:495] 3 3 4 3 4 4 4 3 4 4 ...
## $ un06 : num [1:495] 3 3 2 1 1 1 4 1 4 4 ...
## $ un07 : num [1:495] 2 3 2 2 4 4 3 1 2 3 ...
## $ un08 : num [1:495] 2 3 3 3 4 4 4 4 4 3 ...
## $ un09 : num [1:495] 2 1 4 2 4 4 4 1 2 4 ...
## $ un10 : num [1:495] 3 3 3 2 4 3 4 2 2 4 ...
## $ un11 : num [1:495] 3 2 3 2 4 4 3 3 4 3 ...
## $ un12 : num [1:495] 3 2 3 3 4 4 4 3 4 3 ...
## $ gt01 : num [1:495] 3 3 3 2 3 2 1 1 1 2 ...
## $ gt02 : num [1:495] 3 2 3 3 3 3 1 1 1 2 ...
## $ gt03 : num [1:495] 3 3 3 3 3 2 1 1 1 3 ...
## $ gt04 : num [1:495] 3 2 3 2 3 4 1 1 2 2 ...
## $ gt05 : num [1:495] 2 3 2 2 1 4 0 2 0 2 ...
## $ gt06 : num [1:495] 2 3 3 3 3 3 3 1 1 3 ...
## $ socnet : num [1:495] 1 0 1 1 1 1 1 1 1 1 ...
## $ vk : num [1:495] 1 -2 1 1 1 1 1 1 1 1 ...
## $ fb : num [1:495] 0 -2 1 1 1 0 0 1 0 1 ...
## $ tw : num [1:495] 0 -2 1 0 0 0 0 0 0 0 ...
## $ in : num [1:495] 1 -2 1 1 0 1 1 1 1 1 ...
## $ tt : num [1:495] 0 -2 0 1 0 0 0 0 0 0 ...
## $ yt : num [1:495] 0 -2 1 1 0 0 0 1 1 1 ...
## $ freqvk : num [1:495] 3 -2 3 2 3 3 3 3 3 3 ...
## $ freqfb : num [1:495] -2 -2 2 2 1 -2 -2 0 -2 3 ...
## $ freqtw : num [1:495] -2 -2 2 -2 -2 -2 -2 -2 -2 -2 ...
## $ freqin : num [1:495] 3 -2 3 1 -2 3 3 2 3 3 ...
## $ freqtt : num [1:495] -2 -2 -2 1 -2 -2 -2 -2 -2 -2 ...
## $ freqyt : num [1:495] -2 -2 2 2 -2 -2 -2 2 2 3 ...
## $ expvk : num [1:495] 0 -2 4 3 5 3 5 4 3 3 ...
## $ expfb : num [1:495] -2 -2 3 3 4 -2 -2 2 -2 2 ...
## $ exptw : num [1:495] -2 -2 2 -2 -2 -2 -2 -2 -2 -2 ...
## $ expin : num [1:495] 0 -2 4 4 -2 4 5 2 2 4 ...
## $ exptt : num [1:495] -2 -2 -2 4 -2 -2 -2 -2 -2 -2 ...
## $ expyt : num [1:495] -2 -2 3 4 -2 -2 -2 4 2 3 ...
## $ dighelp : num [1:495] 1 0 1 1 1 1 1 1 1 1 ...
## $ siri : num [1:495] 0 -2 0 1 0 0 1 0 1 0 ...
## [list output truncated]
## - attr(*, "spec")=
## .. cols(
## .. id = col_character(),
## .. e_dighelp = col_double(),
## .. n_dighelp = col_double(),
## .. e_socnet = col_double(),
## .. f_socnet = col_double(),
## .. n_socnet = col_double(),
## .. gt_score = col_double(),
## .. pr01 = col_double(),
## .. pr02 = col_double(),
## .. pr03 = col_double(),
## .. pr04 = col_double(),
## .. pr05 = col_double(),
## .. pr06 = col_double(),
## .. pr07 = col_double(),
## .. pr08 = col_double(),
## .. pr09 = col_double(),
## .. pr10 = col_double(),
## .. co01 = col_double(),
## .. co02 = col_double(),
## .. co03 = col_double(),
## .. co04 = col_double(),
## .. co05 = col_double(),
## .. co06 = col_double(),
## .. co07 = col_double(),
## .. co08 = col_double(),
## .. co09 = col_double(),
## .. co10 = col_double(),
## .. ut01 = col_double(),
## .. ut02 = col_double(),
## .. ut03 = col_double(),
## .. ut04 = col_double(),
## .. ut05 = col_double(),
## .. ut06 = col_double(),
## .. ut07 = col_double(),
## .. ut08 = col_double(),
## .. ut09 = col_double(),
## .. ut10 = col_double(),
## .. ut11 = col_double(),
## .. ut12 = col_double(),
## .. fa01 = col_double(),
## .. fa02 = col_double(),
## .. fa03 = col_double(),
## .. fa04 = col_double(),
## .. fa05 = col_double(),
## .. fa06 = col_double(),
## .. fa07 = col_double(),
## .. fa08 = col_double(),
## .. fa09 = col_double(),
## .. fa10 = col_double(),
## .. de01 = col_double(),
## .. de02 = col_double(),
## .. de03 = col_double(),
## .. de04 = col_double(),
## .. de05 = col_double(),
## .. de06 = col_double(),
## .. de07 = col_double(),
## .. de08 = col_double(),
## .. de09 = col_double(),
## .. de10 = col_double(),
## .. de11 = col_double(),
## .. un01 = col_double(),
## .. un02 = col_double(),
## .. un03 = col_double(),
## .. un04 = col_double(),
## .. un05 = col_double(),
## .. un06 = col_double(),
## .. un07 = col_double(),
## .. un08 = col_double(),
## .. un09 = col_double(),
## .. un10 = col_double(),
## .. un11 = col_double(),
## .. un12 = col_double(),
## .. gt01 = col_double(),
## .. gt02 = col_double(),
## .. gt03 = col_double(),
## .. gt04 = col_double(),
## .. gt05 = col_double(),
## .. gt06 = col_double(),
## .. socnet = col_double(),
## .. vk = col_double(),
## .. fb = col_double(),
## .. tw = col_double(),
## .. `in` = col_double(),
## .. tt = col_double(),
## .. yt = col_double(),
## .. freqvk = col_double(),
## .. freqfb = col_double(),
## .. freqtw = col_double(),
## .. freqin = col_double(),
## .. freqtt = col_double(),
## .. freqyt = col_double(),
## .. expvk = col_double(),
## .. expfb = col_double(),
## .. exptw = col_double(),
## .. expin = col_double(),
## .. exptt = col_double(),
## .. expyt = col_double(),
## .. dighelp = col_double(),
## .. siri = col_double(),
## .. alice = col_double(),
## .. salut = col_double(),
## .. oleg = col_double(),
## .. alex = col_double(),
## .. mia = col_double(),
## .. mts = col_double(),
## .. ggle = col_double(),
## .. oth = col_double(),
## .. oth_text = col_character(),
## .. expsiri = col_double(),
## .. expalice = col_double(),
## .. expsalut = col_double(),
## .. expoleg = col_double(),
## .. expalex = col_double(),
## .. expmia = col_double(),
## .. expmts = col_double(),
## .. expggle = col_double(),
## .. expoth = col_double(),
## .. expoth_text = col_character(),
## .. selfdrcar = col_double(),
## .. selfdrexp = col_double(),
## .. selfdrsafe = col_double(),
## .. eduai = col_double(),
## .. eduaiexp = col_double(),
## .. age = col_double(),
## .. sex = col_character(),
## .. edulvl1 = col_character(),
## .. spec1 = col_character(),
## .. edu2 = col_double(),
## .. edulvl2 = col_character(),
## .. spec2 = col_character(),
## .. jobfield = col_character(),
## .. jobpos = col_character(),
## .. city = col_character()
## .. )
Vectors of TAIA items:
pr_items_0 <- colnames(taia)[8:17]
co_items_0 <- colnames(taia)[18:27]
ut_items_0 <- colnames(taia)[28:39]
fa_items_0 <- colnames(taia)[40:49]
de_items_0 <- colnames(taia)[50:60]
un_items_0 <- colnames(taia)[61:72]
taia_items_0 <- colnames(taia)[8:72]Vector of GT items:
Column names for further formatting:
col_names <- c("", "Num. of obs.", "Mean", "SD",
"Median", "Trimmed Mean", "MAD",
"Min", "Max", "Range",
"Skewness", "Kurtuosis", "SE")
total_colnames <- c("Alpha", "Standardized Alpha", "Guttman's Lambda 6",
"Average interitem correlation", "S/N",
"Alpha SE", "Scale Mean", "Total Score SD",
"Median interitem correlation")
item_stats_colnames <- c("Num. of Obs.", "Discrimination",
"Std Cor",
"Cor Overlap Corrected",
"Cor if drop",
"Difficulty", "SD")
alpha_drop_colnames <- c("Alpha", "Standardized Alpha",
"Guttman's Lambda 6", "Average interitem correlation",
"S/N", "Alpha SE", "Var(r)","Median interitem correlation")taia %>%
select(all_of(pr_items_0)) %>%
describe() %>%
kable(caption = "Predictability", label = 1, digits = 2, col.names = col_names)| Num. of obs. | Mean | SD | Median | Trimmed Mean | MAD | Min | Max | Range | Skewness | Kurtuosis | SE | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| pr01 | 1 | 495 | 2.84 | 0.99 | 3 | 2.87 | 1.48 | 0 | 5 | 5 | -0.28 | 0.37 | 0.04 |
| pr02 | 2 | 495 | 2.73 | 0.97 | 3 | 2.77 | 1.48 | 0 | 5 | 5 | -0.19 | 0.10 | 0.04 |
| pr03 | 3 | 495 | 2.89 | 1.01 | 3 | 2.91 | 1.48 | 0 | 5 | 5 | -0.15 | -0.05 | 0.05 |
| pr04 | 4 | 495 | 2.84 | 1.04 | 3 | 2.87 | 1.48 | 0 | 5 | 5 | -0.18 | -0.04 | 0.05 |
| pr05 | 5 | 495 | 2.22 | 1.20 | 2 | 2.22 | 1.48 | 0 | 5 | 5 | 0.03 | -0.31 | 0.05 |
| pr06 | 6 | 495 | 3.04 | 1.07 | 3 | 3.06 | 1.48 | 0 | 5 | 5 | -0.26 | 0.07 | 0.05 |
| pr07 | 7 | 495 | 2.59 | 1.11 | 3 | 2.61 | 1.48 | 0 | 5 | 5 | -0.15 | -0.10 | 0.05 |
| pr08 | 8 | 495 | 3.05 | 0.91 | 3 | 3.09 | 0.00 | 0 | 5 | 5 | -0.56 | 1.26 | 0.04 |
| pr09 | 9 | 495 | 2.89 | 0.95 | 3 | 2.94 | 0.00 | 0 | 5 | 5 | -0.50 | 1.05 | 0.04 |
| pr10 | 10 | 495 | 2.83 | 1.04 | 3 | 2.90 | 1.48 | 0 | 5 | 5 | -0.41 | 0.28 | 0.05 |
taia %>% select(all_of(pr_items_0)) %>%
pivot_longer(cols = all_of(pr_items_0)) %>%
ggplot(aes(value)) +
geom_bar(fill = "darkred") +
facet_wrap(~ name) +
scale_x_discrete(limits = 0:5) +
labs(x = "Score", y = "Number of observations",
title = "Predictability") +
theme(plot.title = element_text(hjust = .5))## Warning: Continuous limits supplied to discrete scale.
## Did you mean `limits = factor(...)` or `scale_*_continuous()`?
pr01 OK
pr02 OK
pr03 OK
pr04 OK
pr05 positive skewness
pr06 OK
pr07 OK
pr08 high kurtosis
pr09 high kurtosis
pr10 OKtaia %>%
select(all_of(co_items_0)) %>%
describe() %>%
kable(caption = "Consistency", label = 2, digits = 2, col.names = col_names)| Num. of obs. | Mean | SD | Median | Trimmed Mean | MAD | Min | Max | Range | Skewness | Kurtuosis | SE | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| co01 | 1 | 495 | 2.49 | 1.08 | 3 | 2.51 | 1.48 | 0 | 5 | 5 | -0.17 | 0.13 | 0.05 |
| co02 | 2 | 495 | 2.51 | 1.04 | 3 | 2.53 | 1.48 | 0 | 5 | 5 | -0.19 | -0.06 | 0.05 |
| co03 | 3 | 495 | 2.86 | 1.02 | 3 | 2.92 | 1.48 | 0 | 5 | 5 | -0.40 | 0.31 | 0.05 |
| co04 | 4 | 495 | 3.47 | 1.09 | 4 | 3.54 | 1.48 | 0 | 5 | 5 | -0.57 | 0.32 | 0.05 |
| co05 | 5 | 495 | 2.20 | 1.11 | 2 | 2.17 | 1.48 | 0 | 5 | 5 | 0.10 | -0.17 | 0.05 |
| co06 | 6 | 495 | 2.52 | 1.11 | 3 | 2.53 | 1.48 | 0 | 5 | 5 | -0.13 | -0.15 | 0.05 |
| co07 | 7 | 495 | 1.59 | 1.13 | 2 | 1.52 | 1.48 | 0 | 5 | 5 | 0.54 | 0.12 | 0.05 |
| co08 | 8 | 495 | 1.90 | 1.04 | 2 | 1.86 | 1.48 | 0 | 5 | 5 | 0.40 | 0.28 | 0.05 |
| co09 | 9 | 495 | 2.05 | 1.07 | 2 | 2.01 | 1.48 | 0 | 5 | 5 | 0.35 | 0.12 | 0.05 |
| co10 | 10 | 495 | 2.44 | 1.10 | 2 | 2.44 | 1.48 | 0 | 5 | 5 | -0.04 | -0.06 | 0.05 |
taia %>% select(all_of(co_items_0)) %>%
pivot_longer(cols = all_of(co_items_0)) %>%
ggplot(aes(value)) +
geom_bar(fill = "chocolate3") +
facet_wrap(~ name) +
scale_x_discrete(limits = 0:5) +
labs(x = "Score", y = "Number of observations",
title = "Consistency") +
theme(plot.title = element_text(hjust = .5))## Warning: Continuous limits supplied to discrete scale.
## Did you mean `limits = factor(...)` or `scale_*_continuous()`?
co01 OK
co02 OK
co03 high kurtosis
co04 high negative skewness
co05 positive skewness
co06 positive skewness
co07 high positive skewness
co08 positive skewness
co09positive skewness
co10 positive skewness
taia %>%
select(all_of(ut_items_0)) %>%
describe() %>%
kable(caption = "Utility", label = 3, digits = 2, col.names = col_names)| Num. of obs. | Mean | SD | Median | Trimmed Mean | MAD | Min | Max | Range | Skewness | Kurtuosis | SE | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ut01 | 1 | 495 | 3.78 | 1.05 | 4 | 3.88 | 1.48 | 0 | 5 | 5 | -0.86 | 1.12 | 0.05 |
| ut02 | 2 | 495 | 3.52 | 1.05 | 3 | 3.59 | 1.48 | 0 | 5 | 5 | -0.53 | 0.50 | 0.05 |
| ut03 | 3 | 495 | 3.56 | 1.11 | 4 | 3.64 | 1.48 | 0 | 5 | 5 | -0.57 | 0.15 | 0.05 |
| ut04 | 4 | 495 | 3.09 | 1.11 | 3 | 3.15 | 1.48 | 0 | 5 | 5 | -0.43 | 0.03 | 0.05 |
| ut05 | 5 | 495 | 3.05 | 1.21 | 3 | 3.09 | 1.48 | 0 | 5 | 5 | -0.33 | -0.15 | 0.05 |
| ut06 | 6 | 495 | 3.27 | 1.10 | 3 | 3.31 | 1.48 | 0 | 5 | 5 | -0.61 | 0.68 | 0.05 |
| ut07 | 7 | 495 | 3.20 | 1.13 | 3 | 3.23 | 1.48 | 0 | 5 | 5 | -0.28 | -0.22 | 0.05 |
| ut08 | 8 | 495 | 3.44 | 1.05 | 3 | 3.49 | 1.48 | 0 | 5 | 5 | -0.55 | 0.44 | 0.05 |
| ut09 | 9 | 495 | 3.18 | 1.17 | 3 | 3.23 | 1.48 | 0 | 5 | 5 | -0.47 | 0.16 | 0.05 |
| ut10 | 10 | 495 | 2.17 | 1.11 | 2 | 2.16 | 1.48 | 0 | 5 | 5 | 0.08 | -0.23 | 0.05 |
| ut11 | 11 | 495 | 2.67 | 1.23 | 3 | 2.69 | 1.48 | 0 | 5 | 5 | -0.11 | -0.41 | 0.06 |
| ut12 | 12 | 495 | 3.16 | 1.15 | 3 | 3.21 | 1.48 | 0 | 5 | 5 | -0.42 | 0.03 | 0.05 |
taia %>% select(all_of(ut_items_0)) %>%
pivot_longer(cols = all_of(ut_items_0)) %>%
ggplot(aes(value)) +
geom_bar(fill = "goldenrod3") +
facet_wrap(~ name) +
scale_x_discrete(limits = 0:5) +
labs(x = "Score", y = "Number of observations",
title = "Utility") +
theme(plot.title = element_text(hjust = .5))## Warning: Continuous limits supplied to discrete scale.
## Did you mean `limits = factor(...)` or `scale_*_continuous()`?
ut01 extremely high negative skewness
ut02 high negative skewness
ut03 high negative skewness
ut04 negative skewness
ut05 OK
ut06 negative skewness
ut07 light negative skewness
ut08 negative skewness
ut09 negative skewness
ut10 positive skewness
ut11 OK
ut12 negative skewness
taia %>%
select(all_of(fa_items_0)) %>%
describe() %>%
kable(caption = "Faith", label = 4, digits = 2, col.names = col_names)| Num. of obs. | Mean | SD | Median | Trimmed Mean | MAD | Min | Max | Range | Skewness | Kurtuosis | SE | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fa01 | 1 | 495 | 2.42 | 1.10 | 2 | 2.42 | 1.48 | 0 | 5 | 5 | -0.02 | -0.29 | 0.05 |
| fa02 | 2 | 495 | 2.16 | 1.18 | 2 | 2.15 | 1.48 | 0 | 5 | 5 | 0.18 | -0.42 | 0.05 |
| fa03 | 3 | 495 | 1.51 | 1.13 | 1 | 1.42 | 1.48 | 0 | 5 | 5 | 0.66 | 0.16 | 0.05 |
| fa04 | 4 | 495 | 1.57 | 1.08 | 1 | 1.51 | 1.48 | 0 | 5 | 5 | 0.55 | 0.17 | 0.05 |
| fa05 | 5 | 495 | 2.46 | 1.10 | 2 | 2.48 | 1.48 | 0 | 5 | 5 | -0.15 | -0.10 | 0.05 |
| fa06 | 6 | 495 | 2.47 | 1.08 | 3 | 2.49 | 1.48 | 0 | 5 | 5 | -0.18 | 0.06 | 0.05 |
| fa07 | 7 | 495 | 2.37 | 1.09 | 2 | 2.38 | 1.48 | 0 | 5 | 5 | -0.14 | -0.17 | 0.05 |
| fa08 | 8 | 495 | 2.21 | 1.14 | 2 | 2.17 | 1.48 | 0 | 5 | 5 | 0.28 | -0.13 | 0.05 |
| fa09 | 9 | 495 | 2.29 | 1.18 | 2 | 2.27 | 1.48 | 0 | 5 | 5 | 0.15 | -0.40 | 0.05 |
| fa10 | 10 | 495 | 2.64 | 1.20 | 3 | 2.62 | 1.48 | 0 | 5 | 5 | 0.06 | -0.28 | 0.05 |
taia %>% select(all_of(fa_items_0)) %>%
pivot_longer(cols = all_of(fa_items_0)) %>%
ggplot(aes(value)) +
geom_bar(fill = "darkgreen") +
facet_wrap(~ name) +
scale_x_discrete(limits = 0:5) +
labs(x = "Score", y = "Number of observations",
title = "Faith") +
theme(plot.title = element_text(hjust = .5))## Warning: Continuous limits supplied to discrete scale.
## Did you mean `limits = factor(...)` or `scale_*_continuous()`?
fa01 OK
fa02 light positive skewness
fa03 hard positive skewness
fa04 hard positive skewness
fa05 OK
fa06 OK
fa07 OK
fa08 light positive skewness
fa09 OK
fa10 OK
taia %>%
select(all_of(de_items_0)) %>%
describe() %>%
kable(caption = "Dependability", label = 5, digits = 2, col.names = col_names)| Num. of obs. | Mean | SD | Median | Trimmed Mean | MAD | Min | Max | Range | Skewness | Kurtuosis | SE | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| de01 | 1 | 495 | 2.59 | 1.10 | 3 | 2.64 | 1.48 | 0 | 5 | 5 | -0.42 | 0.08 | 0.05 |
| de02 | 2 | 495 | 2.17 | 1.15 | 2 | 2.18 | 1.48 | 0 | 5 | 5 | 0.00 | -0.34 | 0.05 |
| de03 | 3 | 495 | 2.17 | 1.19 | 2 | 2.17 | 1.48 | 0 | 5 | 5 | 0.04 | -0.30 | 0.05 |
| de04 | 4 | 495 | 1.90 | 1.05 | 2 | 1.85 | 1.48 | 0 | 5 | 5 | 0.55 | 0.66 | 0.05 |
| de05 | 5 | 495 | 3.57 | 1.16 | 4 | 3.68 | 1.48 | 0 | 5 | 5 | -0.78 | 0.48 | 0.05 |
| de06 | 6 | 495 | 2.23 | 1.23 | 2 | 2.25 | 1.48 | 0 | 5 | 5 | 0.01 | -0.43 | 0.06 |
| de07 | 7 | 495 | 2.82 | 1.00 | 3 | 2.86 | 1.48 | 0 | 5 | 5 | -0.30 | 0.31 | 0.04 |
| de08 | 8 | 495 | 2.65 | 1.06 | 3 | 2.70 | 1.48 | 0 | 5 | 5 | -0.40 | 0.14 | 0.05 |
| de09 | 9 | 495 | 3.44 | 1.20 | 4 | 3.54 | 1.48 | 0 | 5 | 5 | -0.61 | -0.14 | 0.05 |
| de10 | 10 | 495 | 2.25 | 1.18 | 2 | 2.28 | 1.48 | 0 | 5 | 5 | -0.22 | -0.42 | 0.05 |
| de11 | 11 | 495 | 2.31 | 1.20 | 2 | 2.31 | 1.48 | 0 | 5 | 5 | 0.00 | -0.52 | 0.05 |
taia %>% select(all_of(de_items_0)) %>%
pivot_longer(cols = all_of(de_items_0)) %>%
ggplot(aes(value)) +
geom_bar(fill = "darkblue") +
facet_wrap(~ name) +
scale_x_discrete(limits = 0:5) +
labs(x = "Score", y = "Number of observations",
title = "Dependability") +
theme(plot.title = element_text(hjust = .5))## Warning: Continuous limits supplied to discrete scale.
## Did you mean `limits = factor(...)` or `scale_*_continuous()`?
de01 negative skewness
de02 OK
de03 OK
de04 hard positive skewness
de05 hard negative skewness
de06 negative kurtosis
de07 OK
de08 OK
de09 negative skewness
de10 negative kurtosis
de11 negative kurtosis
taia %>%
select(all_of(un_items_0)) %>%
describe() %>%
kable(caption = "Understanding", label = 6, digits = 2, col.names = col_names)| Num. of obs. | Mean | SD | Median | Trimmed Mean | MAD | Min | Max | Range | Skewness | Kurtuosis | SE | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| un01 | 1 | 495 | 2.93 | 1.05 | 3 | 3.01 | 1.48 | 0 | 5 | 5 | -0.48 | 0.31 | 0.05 |
| un02 | 2 | 495 | 2.47 | 1.14 | 3 | 2.49 | 1.48 | 0 | 5 | 5 | -0.17 | -0.27 | 0.05 |
| un03 | 3 | 495 | 3.02 | 1.17 | 3 | 3.10 | 1.48 | 0 | 5 | 5 | -0.55 | 0.01 | 0.05 |
| un04 | 4 | 495 | 2.61 | 1.09 | 3 | 2.65 | 1.48 | 0 | 5 | 5 | -0.33 | -0.22 | 0.05 |
| un05 | 5 | 495 | 2.82 | 1.10 | 3 | 2.90 | 1.48 | 0 | 5 | 5 | -0.51 | 0.24 | 0.05 |
| un06 | 6 | 495 | 2.29 | 1.23 | 2 | 2.26 | 1.48 | 0 | 5 | 5 | 0.19 | -0.62 | 0.06 |
| un07 | 7 | 495 | 2.13 | 1.18 | 2 | 2.14 | 1.48 | 0 | 5 | 5 | -0.02 | -0.54 | 0.05 |
| un08 | 8 | 495 | 2.90 | 1.16 | 3 | 2.96 | 1.48 | 0 | 5 | 5 | -0.45 | 0.00 | 0.05 |
| un09 | 9 | 495 | 2.32 | 1.23 | 2 | 2.37 | 1.48 | 0 | 5 | 5 | -0.18 | -0.75 | 0.06 |
| un10 | 10 | 495 | 2.24 | 1.15 | 2 | 2.23 | 1.48 | 0 | 5 | 5 | 0.05 | -0.44 | 0.05 |
| un11 | 11 | 495 | 2.63 | 1.20 | 3 | 2.66 | 1.48 | 0 | 5 | 5 | -0.25 | -0.37 | 0.05 |
| un12 | 12 | 495 | 2.89 | 1.12 | 3 | 2.96 | 1.48 | 0 | 5 | 5 | -0.46 | 0.13 | 0.05 |
taia %>% select(all_of(un_items_0)) %>%
pivot_longer(cols = all_of(un_items_0)) %>%
ggplot(aes(value)) +
geom_bar(fill = "purple4") +
facet_wrap(~ name) +
scale_x_discrete(limits = 0:5) +
labs(x = "Score", y = "Number of observations",
title = "Understanding") +
theme(plot.title = element_text(hjust = .5))## Warning: Continuous limits supplied to discrete scale.
## Did you mean `limits = factor(...)` or `scale_*_continuous()`?
un01 OK
un02 OK
un03 negative skewness
un04 negative skewness
un05 positive kurtosis
un06 negative kurtosis
un07 negative kurtosis
un08 OK
un09 extra negative kurtosis
un10 negative kurtosis
un11 OK
un12 OK
ggcorrplot(cor(taia %>% select(all_of(pr_items_0))),
type = "lower", lab = TRUE, lab_size = 3,
colors = c("indianred1", "white", "royalblue1"),
title = "Predictability. Interitems correlations",
show.legend = FALSE)ggcorrplot(cor(taia %>% select(all_of(co_items_0))),
type = "lower", lab = TRUE, lab_size = 3,
colors = c("indianred1", "white", "royalblue1"),
title = "Consistency. Interitems correlations",
show.legend = FALSE)ggcorrplot(cor(taia %>% select(all_of(ut_items_0))),
type = "lower", lab = TRUE, lab_size = 3,
colors = c("indianred1", "white", "royalblue1"),
title = "Utility. Interitems correlations",
show.legend = FALSE)ggcorrplot(cor(taia %>% select(all_of(fa_items_0))),
type = "lower", lab = TRUE, lab_size = 3,
colors = c("indianred1", "white", "royalblue1"),
title = "Faith. Interitems correlations",
show.legend = FALSE)ggcorrplot(cor(taia %>% select(all_of(taia_items_0))),
type = "lower",
colors = c("indianred1", "white", "royalblue1"),
title = "TAIA. Interitems correlations", tl.cex = 5, tl.srt = 90,
legend.title = "Value")qgraph::qgraph(
cor(taia %>% select(all_of(taia_items_0))),
layout = "spring",
posCol = "royalblue",
negCol = "indianred"
)pr1 <- psych::alpha(
taia %>% select(all_of(pr_items_0)),
cumulative = TRUE,
title = "Predictability Factor",
check.keys = FALSE
)kable(pr1$total,
caption = "Perdictability. Subscale statistics",
label = 7, digits = 2,
col.names = total_colnames
)| Alpha | Standardized Alpha | Guttman’s Lambda 6 | Average interitem correlation | S/N | Alpha SE | Scale Mean | Total Score SD | Median interitem correlation | |
|---|---|---|---|---|---|---|---|---|---|
| 0.81 | 0.81 | 0.82 | 0.3 | 4.25 | 0.01 | 27.92 | 6.23 | 0.33 |
pr1$item.stats$mean <- pr1$item.stats$mean / 5
kable(pr1$item.stats,
caption = "Predictability. Items statistics",
label = 8, digits = 2,
col.names = item_stats_colnames)| Num. of Obs. | Discrimination | Std Cor | Cor Overlap Corrected | Cor if drop | Difficulty | SD | |
|---|---|---|---|---|---|---|---|
| pr01 | 495 | 0.79 | 0.80 | 0.80 | 0.72 | 0.57 | 0.99 |
| pr02 | 495 | 0.66 | 0.66 | 0.62 | 0.56 | 0.55 | 0.97 |
| pr03 | 495 | 0.45 | 0.45 | 0.37 | 0.31 | 0.58 | 1.01 |
| pr04 | 495 | 0.32 | 0.32 | 0.21 | 0.16 | 0.57 | 1.04 |
| pr05 | 495 | 0.61 | 0.59 | 0.51 | 0.46 | 0.44 | 1.20 |
| pr06 | 495 | 0.62 | 0.62 | 0.56 | 0.50 | 0.61 | 1.07 |
| pr07 | 495 | 0.74 | 0.73 | 0.70 | 0.63 | 0.52 | 1.11 |
| pr08 | 495 | 0.72 | 0.73 | 0.71 | 0.64 | 0.61 | 0.91 |
| pr09 | 495 | 0.61 | 0.62 | 0.56 | 0.50 | 0.58 | 0.95 |
| pr10 | 495 | 0.55 | 0.56 | 0.47 | 0.42 | 0.57 | 1.04 |
pr1$item.stats %>%
ggplot(aes(x = row.names(pr1$item.stats))) +
geom_point(aes(y = mean), color = "darkblue", size = 3) +
geom_point(aes(y = raw.r), color = "darkred", size = 2) +
geom_hline(yintercept = 0.1, color = "darkblue") +
geom_hline(yintercept = 0.9, color = "darkblue") +
geom_hline(yintercept = 0.25, color = "darkred") +
geom_hline(yintercept = 0, color = "black") +
labs(x = "Item", y = "Value",
title = "Predictability. Items characteristics",
subtitle = "Difficulty (blue) and Dicrimination (red)") +
theme(plot.title = element_text(hjust = .5),
plot.subtitle = element_text(hjust = .5))kable(pr1$alpha.drop,
caption = "Predictability. Subscale statistics when item drop",
label = 9, digits = 2, col.names = alpha_drop_colnames)| Alpha | Standardized Alpha | Guttman’s Lambda 6 | Average interitem correlation | S/N | Alpha SE | Var(r) | Median interitem correlation | |
|---|---|---|---|---|---|---|---|---|
| pr01 | 0.76 | 0.77 | 0.78 | 0.27 | 3.27 | 0.02 | 0.02 | 0.29 |
| pr02 | 0.78 | 0.79 | 0.80 | 0.29 | 3.66 | 0.01 | 0.03 | 0.32 |
| pr03 | 0.81 | 0.81 | 0.81 | 0.32 | 4.33 | 0.01 | 0.03 | 0.36 |
| pr04 | 0.82 | 0.83 | 0.82 | 0.35 | 4.78 | 0.01 | 0.02 | 0.36 |
| pr05 | 0.79 | 0.80 | 0.81 | 0.30 | 3.88 | 0.01 | 0.03 | 0.33 |
| pr06 | 0.79 | 0.79 | 0.81 | 0.30 | 3.79 | 0.01 | 0.03 | 0.33 |
| pr07 | 0.77 | 0.78 | 0.79 | 0.28 | 3.46 | 0.02 | 0.02 | 0.30 |
| pr08 | 0.77 | 0.78 | 0.79 | 0.28 | 3.46 | 0.02 | 0.02 | 0.30 |
| pr09 | 0.79 | 0.79 | 0.80 | 0.30 | 3.80 | 0.01 | 0.03 | 0.35 |
| pr10 | 0.80 | 0.80 | 0.81 | 0.31 | 3.99 | 0.01 | 0.03 | 0.35 |
kable(pr1$response.freq,
caption = "Predictability. Non missing response frequency for each item",
label = 10, digits = 2)| 0 | 1 | 2 | 3 | 4 | 5 | miss | |
|---|---|---|---|---|---|---|---|
| pr01 | 0.02 | 0.06 | 0.24 | 0.45 | 0.19 | 0.04 | 0 |
| pr02 | 0.01 | 0.08 | 0.28 | 0.43 | 0.17 | 0.03 | 0 |
| pr03 | 0.01 | 0.06 | 0.26 | 0.40 | 0.22 | 0.05 | 0 |
| pr04 | 0.02 | 0.07 | 0.27 | 0.38 | 0.21 | 0.05 | 0 |
| pr05 | 0.09 | 0.17 | 0.33 | 0.29 | 0.09 | 0.03 | 0 |
| pr06 | 0.02 | 0.06 | 0.20 | 0.41 | 0.23 | 0.08 | 0 |
| pr07 | 0.04 | 0.12 | 0.28 | 0.38 | 0.14 | 0.04 | 0 |
| pr08 | 0.02 | 0.03 | 0.16 | 0.52 | 0.24 | 0.04 | 0 |
| pr09 | 0.02 | 0.05 | 0.18 | 0.53 | 0.17 | 0.04 | 0 |
| pr10 | 0.02 | 0.08 | 0.21 | 0.45 | 0.20 | 0.04 | 0 |
co1 <- psych::alpha(
taia %>% select(all_of(co_items_0)),
cumulative = TRUE,
title = "Consistency Factor",
check.keys = FALSE
)## Warning in psych::alpha(taia %>% select(all_of(co_items_0)), cumulative = TRUE, : Some items were negatively correlated with the total scale and probably
## should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option
## Some items ( co07 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option
kable(co1$total,
caption = "Consistency. Subscale statistics",
label = 11, digits = 2,
col.names = total_colnames)| Alpha | Standardized Alpha | Guttman’s Lambda 6 | Average interitem correlation | S/N | Alpha SE | Scale Mean | Total Score SD | Median interitem correlation | |
|---|---|---|---|---|---|---|---|---|---|
| 0.76 | 0.77 | 0.8 | 0.25 | 3.27 | 0.01 | 24.03 | 6.1 | 0.27 |
co1$item.stats$mean <- co1$item.stats$mean / 5
kable(co1$item.stats,
caption = "Consistency. Items statistics",
label = 12, digits = 2,
col.names = item_stats_colnames)| Num. of Obs. | Discrimination | Std Cor | Cor Overlap Corrected | Cor if drop | Difficulty | SD | |
|---|---|---|---|---|---|---|---|
| co01 | 495 | 0.74 | 0.74 | 0.72 | 0.64 | 0.50 | 1.08 |
| co02 | 495 | 0.68 | 0.69 | 0.65 | 0.57 | 0.50 | 1.04 |
| co03 | 495 | 0.55 | 0.55 | 0.48 | 0.41 | 0.57 | 1.02 |
| co04 | 495 | 0.40 | 0.41 | 0.30 | 0.24 | 0.69 | 1.09 |
| co05 | 495 | 0.79 | 0.78 | 0.79 | 0.70 | 0.44 | 1.11 |
| co06 | 495 | 0.65 | 0.65 | 0.61 | 0.53 | 0.50 | 1.11 |
| co07 | 495 | -0.03 | -0.04 | -0.22 | -0.22 | 0.32 | 1.13 |
| co08 | 495 | 0.45 | 0.46 | 0.36 | 0.30 | 0.38 | 1.04 |
| co09 | 495 | 0.76 | 0.77 | 0.76 | 0.67 | 0.41 | 1.07 |
| co10 | 495 | 0.67 | 0.67 | 0.62 | 0.56 | 0.49 | 1.10 |
co1$item.stats %>%
ggplot(aes(x = row.names(co1$item.stats))) +
geom_point(aes(y = mean), color = "darkblue", size = 3) +
geom_point(aes(y = raw.r), color = "darkred", size = 2) +
geom_hline(yintercept = 0.1, color = "darkblue") +
geom_hline(yintercept = 0.9, color = "darkblue") +
geom_hline(yintercept = 0.25, color = "darkred") +
geom_hline(yintercept = 0, color = "black") +
labs(x = "Item", y = "Value",
title = "Consistency. Items characteristics",
subtitle = "Difficulty (blue) and Dicrimination (red)") +
theme(plot.title = element_text(hjust = .5),
plot.subtitle = element_text(hjust = .5))kable(co1$alpha.drop,
caption = "Consistency. Subscale statistics when item drop",
label = 13, digits = 2,
col.names = alpha_drop_colnames)| Alpha | Standardized Alpha | Guttman’s Lambda 6 | Average interitem correlation | S/N | Alpha SE | Var(r) | Median interitem correlation | |
|---|---|---|---|---|---|---|---|---|
| co01 | 0.71 | 0.72 | 0.76 | 0.22 | 2.52 | 0.02 | 0.06 | 0.26 |
| co02 | 0.72 | 0.73 | 0.77 | 0.23 | 2.66 | 0.02 | 0.06 | 0.26 |
| co03 | 0.74 | 0.75 | 0.79 | 0.25 | 2.98 | 0.02 | 0.07 | 0.27 |
| co04 | 0.77 | 0.77 | 0.80 | 0.27 | 3.36 | 0.01 | 0.06 | 0.35 |
| co05 | 0.70 | 0.71 | 0.75 | 0.21 | 2.42 | 0.02 | 0.06 | 0.26 |
| co06 | 0.73 | 0.73 | 0.77 | 0.23 | 2.74 | 0.02 | 0.06 | 0.27 |
| co07 | 0.83 | 0.82 | 0.83 | 0.34 | 4.70 | 0.01 | 0.03 | 0.36 |
| co08 | 0.76 | 0.76 | 0.79 | 0.26 | 3.23 | 0.02 | 0.07 | 0.34 |
| co09 | 0.71 | 0.71 | 0.75 | 0.22 | 2.47 | 0.02 | 0.06 | 0.26 |
| co10 | 0.72 | 0.73 | 0.77 | 0.23 | 2.69 | 0.02 | 0.07 | 0.26 |
kable(co1$response.freq,
caption = "Consistency. Non missing response frequency for each item",
label = 14, digits = 2)| 0 | 1 | 2 | 3 | 4 | 5 | miss | |
|---|---|---|---|---|---|---|---|
| co01 | 0.05 | 0.11 | 0.31 | 0.39 | 0.11 | 0.03 | 0 |
| co02 | 0.03 | 0.12 | 0.32 | 0.38 | 0.13 | 0.02 | 0 |
| co03 | 0.02 | 0.07 | 0.22 | 0.44 | 0.21 | 0.04 | 0 |
| co04 | 0.01 | 0.04 | 0.10 | 0.35 | 0.32 | 0.18 | 0 |
| co05 | 0.06 | 0.19 | 0.37 | 0.27 | 0.09 | 0.02 | 0 |
| co06 | 0.04 | 0.14 | 0.28 | 0.38 | 0.13 | 0.03 | 0 |
| co07 | 0.18 | 0.31 | 0.32 | 0.14 | 0.04 | 0.02 | 0 |
| co08 | 0.08 | 0.27 | 0.42 | 0.16 | 0.06 | 0.01 | 0 |
| co09 | 0.06 | 0.24 | 0.40 | 0.22 | 0.06 | 0.02 | 0 |
| co10 | 0.04 | 0.14 | 0.33 | 0.34 | 0.11 | 0.03 | 0 |
ut1 <- psych::alpha(
taia %>% select(all_of(ut_items_0)),
cumulative = TRUE,
title = "Utility Factor",
check.keys = FALSE
)kable(ut1$total,
caption = "Utility. Subscale statistics",
label = 15, digits = 2,
col.names = total_colnames)| Alpha | Standardized Alpha | Guttman’s Lambda 6 | Average interitem correlation | S/N | Alpha SE | Scale Mean | Total Score SD | Median interitem correlation | |
|---|---|---|---|---|---|---|---|---|---|
| 0.86 | 0.86 | 0.87 | 0.34 | 6.17 | 0.01 | 38.09 | 8.44 | 0.37 |
ut1$item.stats$mean <- ut1$item.stats$mean / 5
kable(ut1$item.stats,
caption = "Utility. Items statistics",
label = 16, digits = 2,
col.names = item_stats_colnames)| Num. of Obs. | Discrimination | Std Cor | Cor Overlap Corrected | Cor if drop | Difficulty | SD | |
|---|---|---|---|---|---|---|---|
| ut01 | 495 | 0.77 | 0.78 | 0.78 | 0.71 | 0.76 | 1.05 |
| ut02 | 495 | 0.82 | 0.83 | 0.84 | 0.77 | 0.70 | 1.05 |
| ut03 | 495 | 0.57 | 0.57 | 0.52 | 0.47 | 0.71 | 1.11 |
| ut04 | 495 | 0.51 | 0.51 | 0.44 | 0.40 | 0.62 | 1.11 |
| ut05 | 495 | 0.69 | 0.69 | 0.65 | 0.61 | 0.61 | 1.21 |
| ut06 | 495 | 0.76 | 0.76 | 0.74 | 0.70 | 0.65 | 1.10 |
| ut07 | 495 | 0.63 | 0.63 | 0.59 | 0.54 | 0.64 | 1.13 |
| ut08 | 495 | 0.65 | 0.66 | 0.62 | 0.57 | 0.69 | 1.05 |
| ut09 | 495 | 0.68 | 0.68 | 0.64 | 0.59 | 0.64 | 1.17 |
| ut10 | 495 | 0.17 | 0.17 | 0.06 | 0.04 | 0.43 | 1.11 |
| ut11 | 495 | 0.56 | 0.55 | 0.48 | 0.45 | 0.53 | 1.23 |
| ut12 | 495 | 0.71 | 0.71 | 0.68 | 0.64 | 0.63 | 1.15 |
ut1$item.stats %>%
ggplot(aes(x = row.names(ut1$item.stats))) +
geom_point(aes(y = mean), color = "darkblue", size = 3) +
geom_point(aes(y = raw.r), color = "darkred", size = 2) +
geom_hline(yintercept = 0.1, color = "darkblue") +
geom_hline(yintercept = 0.9, color = "darkblue") +
geom_hline(yintercept = 0.25, color = "darkred") +
geom_hline(yintercept = 0, color = "black") +
labs(x = "Item", y = "Value",
title = "Utility. Items characteristics",
subtitle = "Difficulty (blue) and Dicrimination (red)") +
theme(plot.title = element_text(hjust = .5),
plot.subtitle = element_text(hjust = .5))kable(ut1$alpha.drop,
caption = "Utility. Subscale statistics when item drop",
label = 17, digits = 2,
col.names = alpha_drop_colnames)| Alpha | Standardized Alpha | Guttman’s Lambda 6 | Average interitem correlation | S/N | Alpha SE | Var(r) | Median interitem correlation | |
|---|---|---|---|---|---|---|---|---|
| ut01 | 0.84 | 0.84 | 0.85 | 0.32 | 5.16 | 0.01 | 0.03 | 0.34 |
| ut02 | 0.83 | 0.83 | 0.84 | 0.31 | 5.00 | 0.01 | 0.03 | 0.33 |
| ut03 | 0.85 | 0.85 | 0.86 | 0.35 | 5.84 | 0.01 | 0.04 | 0.41 |
| ut04 | 0.86 | 0.86 | 0.87 | 0.36 | 6.06 | 0.01 | 0.03 | 0.41 |
| ut05 | 0.84 | 0.84 | 0.86 | 0.33 | 5.45 | 0.01 | 0.04 | 0.37 |
| ut06 | 0.84 | 0.84 | 0.85 | 0.32 | 5.21 | 0.01 | 0.03 | 0.33 |
| ut07 | 0.85 | 0.85 | 0.86 | 0.34 | 5.65 | 0.01 | 0.04 | 0.37 |
| ut08 | 0.85 | 0.85 | 0.86 | 0.34 | 5.55 | 0.01 | 0.03 | 0.37 |
| ut09 | 0.84 | 0.85 | 0.86 | 0.33 | 5.49 | 0.01 | 0.03 | 0.37 |
| ut10 | 0.88 | 0.88 | 0.88 | 0.40 | 7.40 | 0.01 | 0.01 | 0.41 |
| ut11 | 0.85 | 0.86 | 0.87 | 0.35 | 5.92 | 0.01 | 0.04 | 0.41 |
| ut12 | 0.84 | 0.84 | 0.86 | 0.33 | 5.37 | 0.01 | 0.03 | 0.35 |
kable(ut1$response.freq,
caption = "Utility. Non missing response frequency for each item",
label = 18, digits = 2)| 0 | 1 | 2 | 3 | 4 | 5 | miss | |
|---|---|---|---|---|---|---|---|
| ut01 | 0.01 | 0.01 | 0.06 | 0.29 | 0.34 | 0.28 | 0 |
| ut02 | 0.01 | 0.02 | 0.09 | 0.38 | 0.30 | 0.20 | 0 |
| ut03 | 0.01 | 0.04 | 0.09 | 0.33 | 0.31 | 0.22 | 0 |
| ut04 | 0.02 | 0.08 | 0.15 | 0.40 | 0.27 | 0.09 | 0 |
| ut05 | 0.03 | 0.06 | 0.20 | 0.35 | 0.23 | 0.12 | 0 |
| ut06 | 0.03 | 0.03 | 0.13 | 0.39 | 0.30 | 0.12 | 0 |
| ut07 | 0.01 | 0.05 | 0.19 | 0.35 | 0.26 | 0.14 | 0 |
| ut08 | 0.01 | 0.03 | 0.11 | 0.36 | 0.34 | 0.15 | 0 |
| ut09 | 0.03 | 0.05 | 0.15 | 0.38 | 0.25 | 0.13 | 0 |
| ut10 | 0.07 | 0.19 | 0.37 | 0.26 | 0.09 | 0.02 | 0 |
| ut11 | 0.05 | 0.12 | 0.27 | 0.32 | 0.18 | 0.07 | 0 |
| ut12 | 0.02 | 0.07 | 0.15 | 0.38 | 0.26 | 0.12 | 0 |
fa1 <- psych::alpha(
taia %>% select(all_of(fa_items_0)),
cumulative = TRUE,
title = "Faith Factor",
check.keys = FALSE
)kable(fa1$total,
caption = "Faith. Subscale statistics",
label = 19, digits = 2,
col.names = total_colnames)| Alpha | Standardized Alpha | Guttman’s Lambda 6 | Average interitem correlation | S/N | Alpha SE | Scale Mean | Total Score SD | Median interitem correlation | |
|---|---|---|---|---|---|---|---|---|---|
| 0.77 | 0.77 | 0.81 | 0.25 | 3.26 | 0.02 | 22.1 | 6.41 | 0.24 |
fa1$item.stats$mean <- fa1$item.stats$mean / 5
kable(fa1$item.stats,
caption = "Faith. Items statistics",
label = 20, digits = 2,
col.names = item_stats_colnames)| Num. of Obs. | Discrimination | Std Cor | Cor Overlap Corrected | Cor if drop | Difficulty | SD | |
|---|---|---|---|---|---|---|---|
| fa01 | 495 | 0.76 | 0.76 | 0.76 | 0.67 | 0.48 | 1.10 |
| fa02 | 495 | 0.61 | 0.60 | 0.56 | 0.47 | 0.43 | 1.18 |
| fa03 | 495 | 0.27 | 0.28 | 0.16 | 0.10 | 0.30 | 1.13 |
| fa04 | 495 | 0.41 | 0.42 | 0.33 | 0.26 | 0.31 | 1.08 |
| fa05 | 495 | 0.77 | 0.78 | 0.78 | 0.69 | 0.49 | 1.10 |
| fa06 | 495 | 0.55 | 0.56 | 0.50 | 0.42 | 0.49 | 1.08 |
| fa07 | 495 | 0.42 | 0.42 | 0.31 | 0.26 | 0.47 | 1.09 |
| fa08 | 495 | 0.58 | 0.57 | 0.52 | 0.44 | 0.44 | 1.14 |
| fa09 | 495 | 0.66 | 0.65 | 0.62 | 0.53 | 0.46 | 1.18 |
| fa10 | 495 | 0.63 | 0.62 | 0.57 | 0.50 | 0.53 | 1.20 |
fa1$item.stats %>%
ggplot(aes(x = row.names(fa1$item.stats))) +
geom_point(aes(y = mean), color = "darkblue", size = 3) +
geom_point(aes(y = raw.r), color = "darkred", size = 2) +
geom_hline(yintercept = 0.1, color = "darkblue") +
geom_hline(yintercept = 0.9, color = "darkblue") +
geom_hline(yintercept = 0.25, color = "darkred") +
geom_hline(yintercept = 0, color = "black") +
labs(x = "Item", y = "Value",
title = "Faith. Items characteristics",
subtitle = "Difficulty (blue) and Dicrimination (red)") +
theme(plot.title = element_text(hjust = .5),
plot.subtitle = element_text(hjust = .5))kable(fa1$alpha.drop,
caption = "Faith. Subscale statistics when item drop",
label = 21, digits = 2,
col.names = alpha_drop_colnames)| Alpha | Standardized Alpha | Guttman’s Lambda 6 | Average interitem correlation | S/N | Alpha SE | Var(r) | Median interitem correlation | |
|---|---|---|---|---|---|---|---|---|
| fa01 | 0.71 | 0.71 | 0.76 | 0.22 | 2.47 | 0.02 | 0.04 | 0.22 |
| fa02 | 0.74 | 0.74 | 0.78 | 0.24 | 2.87 | 0.02 | 0.04 | 0.24 |
| fa03 | 0.79 | 0.79 | 0.82 | 0.29 | 3.70 | 0.01 | 0.04 | 0.30 |
| fa04 | 0.77 | 0.77 | 0.81 | 0.27 | 3.30 | 0.02 | 0.04 | 0.26 |
| fa05 | 0.71 | 0.71 | 0.76 | 0.21 | 2.43 | 0.02 | 0.04 | 0.22 |
| fa06 | 0.75 | 0.75 | 0.79 | 0.25 | 2.95 | 0.02 | 0.05 | 0.29 |
| fa07 | 0.77 | 0.77 | 0.81 | 0.27 | 3.30 | 0.02 | 0.05 | 0.30 |
| fa08 | 0.74 | 0.75 | 0.79 | 0.25 | 2.92 | 0.02 | 0.04 | 0.24 |
| fa09 | 0.73 | 0.73 | 0.78 | 0.23 | 2.73 | 0.02 | 0.04 | 0.24 |
| fa10 | 0.74 | 0.74 | 0.79 | 0.24 | 2.79 | 0.02 | 0.04 | 0.24 |
kable(fa1$response.freq,
caption = "Faith. Non missing response frequency for each item",
label = 22, digits = 2)| 0 | 1 | 2 | 3 | 4 | 5 | miss | |
|---|---|---|---|---|---|---|---|
| fa01 | 0.04 | 0.16 | 0.32 | 0.33 | 0.13 | 0.03 | 0 |
| fa02 | 0.08 | 0.21 | 0.36 | 0.21 | 0.12 | 0.02 | 0 |
| fa03 | 0.19 | 0.35 | 0.29 | 0.11 | 0.05 | 0.01 | 0 |
| fa04 | 0.16 | 0.34 | 0.33 | 0.12 | 0.04 | 0.01 | 0 |
| fa05 | 0.05 | 0.12 | 0.33 | 0.34 | 0.13 | 0.03 | 0 |
| fa06 | 0.05 | 0.12 | 0.32 | 0.38 | 0.11 | 0.03 | 0 |
| fa07 | 0.05 | 0.15 | 0.32 | 0.35 | 0.11 | 0.02 | 0 |
| fa08 | 0.05 | 0.20 | 0.38 | 0.23 | 0.10 | 0.03 | 0 |
| fa09 | 0.06 | 0.19 | 0.34 | 0.25 | 0.13 | 0.03 | 0 |
| fa10 | 0.04 | 0.12 | 0.31 | 0.32 | 0.14 | 0.08 | 0 |
de1 <- psych::alpha(
taia %>% select(all_of(de_items_0)),
cumulative = TRUE,
title = "Dependability Factor",
check.keys = FALSE
)## Warning in psych::alpha(taia %>% select(all_of(de_items_0)), cumulative = TRUE, : Some items were negatively correlated with the total scale and probably
## should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option
## Some items ( de04 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option
kable(de1$total,
caption = "Dependability. Subscale statistics",
label = 23, digits = 2,
col.names = total_colnames)| Alpha | Standardized Alpha | Guttman’s Lambda 6 | Average interitem correlation | S/N | Alpha SE | Scale Mean | Total Score SD | Median interitem correlation | |
|---|---|---|---|---|---|---|---|---|---|
| 0.75 | 0.75 | 0.8 | 0.21 | 2.96 | 0.02 | 28.09 | 6.74 | 0.24 |
de1$item.stats$mean <- de1$item.stats$mean / 5
kable(de1$item.stats,
caption = "Dependability. Items statistics",
label = 24, digits = 2,
col.names = item_stats_colnames)| Num. of Obs. | Discrimination | Std Cor | Cor Overlap Corrected | Cor if drop | Difficulty | SD | |
|---|---|---|---|---|---|---|---|
| de01 | 495 | 0.57 | 0.58 | 0.52 | 0.45 | 0.52 | 1.10 |
| de02 | 495 | 0.72 | 0.72 | 0.69 | 0.61 | 0.43 | 1.15 |
| de03 | 495 | 0.66 | 0.66 | 0.61 | 0.54 | 0.43 | 1.19 |
| de04 | 495 | -0.23 | -0.22 | -0.39 | -0.36 | 0.38 | 1.05 |
| de05 | 495 | 0.59 | 0.58 | 0.56 | 0.46 | 0.71 | 1.16 |
| de06 | 495 | 0.67 | 0.67 | 0.62 | 0.55 | 0.45 | 1.23 |
| de07 | 495 | 0.58 | 0.60 | 0.54 | 0.47 | 0.56 | 1.00 |
| de08 | 495 | 0.67 | 0.68 | 0.66 | 0.57 | 0.53 | 1.06 |
| de09 | 495 | 0.45 | 0.44 | 0.39 | 0.30 | 0.69 | 1.20 |
| de10 | 495 | 0.74 | 0.74 | 0.72 | 0.64 | 0.45 | 1.18 |
| de11 | 495 | 0.43 | 0.42 | 0.30 | 0.27 | 0.46 | 1.20 |
de1$item.stats %>%
ggplot(aes(x = row.names(de1$item.stats))) +
geom_point(aes(y = mean), color = "darkblue", size = 3) +
geom_point(aes(y = raw.r), color = "darkred", size = 2) +
geom_hline(yintercept = 0.1, color = "darkblue") +
geom_hline(yintercept = 0.9, color = "darkblue") +
geom_hline(yintercept = 0.25, color = "darkred") +
geom_hline(yintercept = 0, color = "black") +
labs(x = "Item", y = "Value",
title = "Dependability. Items characteristics",
subtitle = "Difficulty (blue) and Dicrimination (red)") +
theme(plot.title = element_text(hjust = .5),
plot.subtitle = element_text(hjust = .5))kable(de1$alpha.drop,
caption = "Dependability. Subscale statistics when item drop",
label = 25, digits = 2,
col.names = alpha_drop_colnames)| Alpha | Standardized Alpha | Guttman’s Lambda 6 | Average interitem correlation | S/N | Alpha SE | Var(r) | Median interitem correlation | |
|---|---|---|---|---|---|---|---|---|
| de01 | 0.73 | 0.72 | 0.78 | 0.21 | 2.59 | 0.02 | 0.07 | 0.22 |
| de02 | 0.71 | 0.70 | 0.76 | 0.19 | 2.32 | 0.02 | 0.07 | 0.22 |
| de03 | 0.72 | 0.71 | 0.77 | 0.20 | 2.44 | 0.02 | 0.07 | 0.23 |
| de04 | 0.82 | 0.82 | 0.84 | 0.31 | 4.49 | 0.01 | 0.02 | 0.32 |
| de05 | 0.73 | 0.72 | 0.76 | 0.21 | 2.59 | 0.02 | 0.07 | 0.22 |
| de06 | 0.71 | 0.71 | 0.77 | 0.19 | 2.42 | 0.02 | 0.07 | 0.22 |
| de07 | 0.73 | 0.72 | 0.78 | 0.20 | 2.56 | 0.02 | 0.07 | 0.22 |
| de08 | 0.71 | 0.70 | 0.76 | 0.19 | 2.39 | 0.02 | 0.06 | 0.22 |
| de09 | 0.75 | 0.74 | 0.78 | 0.22 | 2.88 | 0.02 | 0.07 | 0.30 |
| de10 | 0.70 | 0.69 | 0.76 | 0.19 | 2.28 | 0.02 | 0.06 | 0.22 |
| de11 | 0.75 | 0.75 | 0.80 | 0.23 | 2.93 | 0.02 | 0.08 | 0.32 |
kable(de1$response.freq,
caption = "Dependability. Non missing response frequency for each item",
label = 26, digits = 2)| 0 | 1 | 2 | 3 | 4 | 5 | miss | |
|---|---|---|---|---|---|---|---|
| de01 | 0.05 | 0.11 | 0.24 | 0.43 | 0.14 | 0.03 | 0 |
| de02 | 0.09 | 0.17 | 0.36 | 0.26 | 0.10 | 0.02 | 0 |
| de03 | 0.10 | 0.17 | 0.35 | 0.27 | 0.09 | 0.03 | 0 |
| de04 | 0.07 | 0.26 | 0.45 | 0.14 | 0.05 | 0.02 | 0 |
| de05 | 0.02 | 0.03 | 0.10 | 0.28 | 0.34 | 0.23 | 0 |
| de06 | 0.10 | 0.17 | 0.32 | 0.28 | 0.11 | 0.03 | 0 |
| de07 | 0.02 | 0.06 | 0.27 | 0.42 | 0.20 | 0.03 | 0 |
| de08 | 0.04 | 0.10 | 0.24 | 0.44 | 0.15 | 0.03 | 0 |
| de09 | 0.01 | 0.06 | 0.13 | 0.27 | 0.33 | 0.20 | 0 |
| de10 | 0.10 | 0.15 | 0.30 | 0.34 | 0.10 | 0.02 | 0 |
| de11 | 0.07 | 0.20 | 0.28 | 0.31 | 0.12 | 0.03 | 0 |
un1 <- psych::alpha(
taia %>% select(all_of(un_items_0)),
cumulative = TRUE,
title = "Understanding Factor",
check.keys = FALSE
)kable(un1$total,
caption = "Understanding. Subscale statistics",
label = 27, digits = 2,
col.names = total_colnames)| Alpha | Standardized Alpha | Guttman’s Lambda 6 | Average interitem correlation | S/N | Alpha SE | Scale Mean | Total Score SD | Median interitem correlation | |
|---|---|---|---|---|---|---|---|---|---|
| 0.92 | 0.92 | 0.92 | 0.5 | 12 | 0.01 | 31.24 | 10.15 | 0.51 |
un1$item.stats$mean <- un1$item.stats$mean / 5
kable(un1$item.stats,
caption = "Understanding. Items statistics",
label = 28, digits = 2,
col.names = item_stats_colnames)| Num. of Obs. | Discrimination | Std Cor | Cor Overlap Corrected | Cor if drop | Difficulty | SD | |
|---|---|---|---|---|---|---|---|
| un01 | 495 | 0.75 | 0.75 | 0.73 | 0.70 | 0.59 | 1.05 |
| un02 | 495 | 0.84 | 0.84 | 0.84 | 0.81 | 0.49 | 1.14 |
| un03 | 495 | 0.59 | 0.59 | 0.54 | 0.51 | 0.60 | 1.17 |
| un04 | 495 | 0.76 | 0.76 | 0.73 | 0.71 | 0.52 | 1.09 |
| un05 | 495 | 0.81 | 0.81 | 0.80 | 0.77 | 0.56 | 1.10 |
| un06 | 495 | 0.57 | 0.56 | 0.50 | 0.48 | 0.46 | 1.23 |
| un07 | 495 | 0.72 | 0.72 | 0.69 | 0.66 | 0.43 | 1.18 |
| un08 | 495 | 0.76 | 0.76 | 0.75 | 0.71 | 0.58 | 1.16 |
| un09 | 495 | 0.73 | 0.73 | 0.69 | 0.67 | 0.46 | 1.23 |
| un10 | 495 | 0.75 | 0.75 | 0.72 | 0.69 | 0.45 | 1.15 |
| un11 | 495 | 0.79 | 0.79 | 0.77 | 0.74 | 0.53 | 1.20 |
| un12 | 495 | 0.75 | 0.76 | 0.73 | 0.70 | 0.58 | 1.12 |
un1$item.stats %>%
ggplot(aes(x = row.names(un1$item.stats))) +
geom_point(aes(y = mean), color = "darkblue", size = 3) +
geom_point(aes(y = raw.r), color = "darkred", size = 2) +
geom_hline(yintercept = 0.1, color = "darkblue") +
geom_hline(yintercept = 0.9, color = "darkblue") +
geom_hline(yintercept = 0.25, color = "darkred") +
geom_hline(yintercept = 0, color = "black") +
labs(x = "Item", y = "Value",
title = "Understanding. Items characteristics",
subtitle = "Difficulty (blue) and Dicrimination (red)") +
theme(plot.title = element_text(hjust = .5),
plot.subtitle = element_text(hjust = .5))kable(un1$alpha.drop,
caption = "Understanding. Subscale statistics when item drop",
label = 29, digits = 2,
col.names = alpha_drop_colnames)| Alpha | Standardized Alpha | Guttman’s Lambda 6 | Average interitem correlation | S/N | Alpha SE | Var(r) | Median interitem correlation | |
|---|---|---|---|---|---|---|---|---|
| un01 | 0.91 | 0.92 | 0.91 | 0.50 | 10.87 | 0.01 | 0.01 | 0.51 |
| un02 | 0.91 | 0.91 | 0.91 | 0.48 | 10.26 | 0.01 | 0.01 | 0.50 |
| un03 | 0.92 | 0.92 | 0.92 | 0.52 | 12.05 | 0.01 | 0.01 | 0.54 |
| un04 | 0.91 | 0.92 | 0.92 | 0.50 | 10.82 | 0.01 | 0.01 | 0.51 |
| un05 | 0.91 | 0.91 | 0.91 | 0.49 | 10.46 | 0.01 | 0.01 | 0.50 |
| un06 | 0.92 | 0.92 | 0.92 | 0.53 | 12.30 | 0.01 | 0.01 | 0.54 |
| un07 | 0.92 | 0.92 | 0.92 | 0.50 | 11.10 | 0.01 | 0.01 | 0.51 |
| un08 | 0.91 | 0.92 | 0.91 | 0.50 | 10.80 | 0.01 | 0.01 | 0.51 |
| un09 | 0.92 | 0.92 | 0.92 | 0.50 | 11.06 | 0.01 | 0.01 | 0.51 |
| un10 | 0.91 | 0.92 | 0.92 | 0.50 | 10.93 | 0.01 | 0.01 | 0.51 |
| un11 | 0.91 | 0.91 | 0.91 | 0.49 | 10.62 | 0.01 | 0.01 | 0.50 |
| un12 | 0.91 | 0.92 | 0.92 | 0.50 | 10.86 | 0.01 | 0.01 | 0.50 |
kable(un1$response.freq,
caption = "Understanding. Non missing response frequency for each item",
label = 30, digits = 2)| 0 | 1 | 2 | 3 | 4 | 5 | miss | |
|---|---|---|---|---|---|---|---|
| un01 | 0.02 | 0.07 | 0.19 | 0.43 | 0.24 | 0.05 | 0 |
| un02 | 0.05 | 0.14 | 0.29 | 0.35 | 0.14 | 0.03 | 0 |
| un03 | 0.03 | 0.08 | 0.16 | 0.36 | 0.29 | 0.07 | 0 |
| un04 | 0.03 | 0.13 | 0.24 | 0.40 | 0.17 | 0.02 | 0 |
| un05 | 0.04 | 0.08 | 0.19 | 0.44 | 0.21 | 0.04 | 0 |
| un06 | 0.06 | 0.24 | 0.28 | 0.25 | 0.14 | 0.04 | 0 |
| un07 | 0.09 | 0.21 | 0.30 | 0.29 | 0.09 | 0.02 | 0 |
| un08 | 0.04 | 0.09 | 0.18 | 0.39 | 0.23 | 0.06 | 0 |
| un09 | 0.08 | 0.18 | 0.25 | 0.30 | 0.17 | 0.01 | 0 |
| un10 | 0.06 | 0.20 | 0.33 | 0.27 | 0.12 | 0.02 | 0 |
| un11 | 0.05 | 0.13 | 0.23 | 0.36 | 0.18 | 0.05 | 0 |
| un12 | 0.03 | 0.08 | 0.19 | 0.41 | 0.23 | 0.06 | 0 |
splitHalf(taia %>% select(all_of(pr_items_0)),
raw=F, brute=F, n.sample=100, covar=F,
check.keys=F, key=NULL, use="pairwise")## Split half reliabilities
## Call: splitHalf(r = taia %>% select(all_of(pr_items_0)), raw = F, brute = F,
## n.sample = 100, covar = F, check.keys = F, key = NULL, use = "pairwise")
##
## Maximum split half reliability (lambda 4) = 0.86
## Guttman lambda 6 = 0.82
## Average split half reliability = 0.81
## Guttman lambda 3 (alpha) = 0.81
## Guttman lambda 2 = 0.82
## Minimum split half reliability (beta) = 0.74
## Average interitem r = 0.3 with median = 0.33
splitHalf(taia %>% select(all_of(co_items_0)),
raw=F, brute=F, n.sample=100, covar=F,
check.keys=F, key=NULL, use="pairwise")## Split half reliabilities
## Call: splitHalf(r = taia %>% select(all_of(co_items_0)), raw = F, brute = F,
## n.sample = 100, covar = F, check.keys = F, key = NULL, use = "pairwise")
##
## Maximum split half reliability (lambda 4) = 0.82
## Guttman lambda 6 = 0.8
## Average split half reliability = 0.77
## Guttman lambda 3 (alpha) = 0.77
## Guttman lambda 2 = 0.8
## Minimum split half reliability (beta) = 0.66
## Average interitem r = 0.25 with median = 0.27
splitHalf(taia %>% select(all_of(ut_items_0)),
raw=F, brute=F, n.sample=100, covar=F,
check.keys=F, key=NULL, use="pairwise")## Split half reliabilities
## Call: splitHalf(r = taia %>% select(all_of(ut_items_0)), raw = F, brute = F,
## n.sample = 100, covar = F, check.keys = F, key = NULL, use = "pairwise")
##
## Maximum split half reliability (lambda 4) = 0.9
## Guttman lambda 6 = 0.87
## Average split half reliability = 0.86
## Guttman lambda 3 (alpha) = 0.86
## Guttman lambda 2 = 0.87
## Minimum split half reliability (beta) = 0.82
## Average interitem r = 0.34 with median = 0.37
splitHalf(taia %>% select(all_of(fa_items_0)),
raw=F, brute=F, n.sample=100, covar=F,
check.keys=F, key=NULL, use="pairwise")## Split half reliabilities
## Call: splitHalf(r = taia %>% select(all_of(fa_items_0)), raw = F, brute = F,
## n.sample = 100, covar = F, check.keys = F, key = NULL, use = "pairwise")
##
## Maximum split half reliability (lambda 4) = 0.86
## Guttman lambda 6 = 0.81
## Average split half reliability = 0.77
## Guttman lambda 3 (alpha) = 0.77
## Guttman lambda 2 = 0.79
## Minimum split half reliability (beta) = 0.56
## Average interitem r = 0.25 with median = 0.24
splitHalf(taia %>% select(all_of(de_items_0)),
raw=F, brute=F, n.sample=100, covar=F,
check.keys=F, key=NULL, use="pairwise")## Split half reliabilities
## Call: splitHalf(r = taia %>% select(all_of(de_items_0)), raw = F, brute = F,
## n.sample = 100, covar = F, check.keys = F, key = NULL, use = "pairwise")
##
## Maximum split half reliability (lambda 4) = 0.85
## Guttman lambda 6 = 0.8
## Average split half reliability = 0.74
## Guttman lambda 3 (alpha) = 0.75
## Guttman lambda 2 = 0.78
## Minimum split half reliability (beta) = 0.52
## Average interitem r = 0.21 with median = 0.24
splitHalf(taia %>% select(all_of(un_items_0)),
raw=F, brute=F, n.sample=100, covar=F,
check.keys=F, key=NULL, use="pairwise")## Split half reliabilities
## Call: splitHalf(r = taia %>% select(all_of(un_items_0)), raw = F, brute = F,
## n.sample = 100, covar = F, check.keys = F, key = NULL, use = "pairwise")
##
## Maximum split half reliability (lambda 4) = 0.94
## Guttman lambda 6 = 0.92
## Average split half reliability = 0.92
## Guttman lambda 3 (alpha) = 0.92
## Guttman lambda 2 = 0.92
## Minimum split half reliability (beta) = 0.89
## Average interitem r = 0.5 with median = 0.51
splitHalf(taia %>% select(all_of(taia_items_0)),
raw=F, brute=F, n.sample=100, covar=F,
check.keys=F, key=NULL, use="pairwise")## Split half reliabilities
## Call: splitHalf(r = taia %>% select(all_of(taia_items_0)), raw = F,
## brute = F, n.sample = 100, covar = F, check.keys = F, key = NULL,
## use = "pairwise")
##
## Maximum split half reliability (lambda 4) = 0.96
## Guttman lambda 6 = 0.96
## Average split half reliability = 0.93
## Guttman lambda 3 (alpha) = 0.93
## Guttman lambda 2 = 0.94
## Minimum split half reliability (beta) = 0.83
## Average interitem r = 0.17 with median = 0.18
Excluded items: co07, ut10, de04
Reason: negative discrimination
Stems:
co07 Я могу сказать, что система работает корректно, только протестировав её (R)ut10 Мне кажется, при оформлении услуг через интернет можно справиться и без цифровых помощников (R)de04 Я могу доверять интеллектуальной системе, если я точно понимаю, какие опасности исходят от неё (R)pr_items_1 <- pr_items_0
co_items_1 <- co_items_0[-7]
ut_items_1 <- ut_items_0[-10]
fa_items_1 <- fa_items_0
de_items_1 <- de_items_0[-4]
un_items_1 <- un_items_0
taia_items_1 <- c(pr_items_1,
co_items_1,
ut_items_1,
fa_items_1,
de_items_1,
un_items_1)co2 <- psych::alpha(
taia %>% select(all_of(co_items_1)),
cumulative = TRUE,
title = "Consistency Factor",
check.keys = FALSE
)kable(co2$total,
caption = "Consistency. Subscale statistics",
label = 11, digits = 2,
col.names = total_colnames)| Alpha | Standardized Alpha | Guttman’s Lambda 6 | Average interitem correlation | S/N | Alpha SE | Scale Mean | Total Score SD | Median interitem correlation | |
|---|---|---|---|---|---|---|---|---|---|
| 0.83 | 0.82 | 0.83 | 0.34 | 4.7 | 0.01 | 22.44 | 6.24 | 0.36 |
co2$item.stats$mean <- co2$item.stats$mean / 5
kable(co2$item.stats,
caption = "Consistency. Items statistics",
label = 12, digits = 2,
col.names = item_stats_colnames)| Num. of Obs. | Discrimination | Std Cor | Cor Overlap Corrected | Cor if drop | Difficulty | SD | |
|---|---|---|---|---|---|---|---|
| co01 | 495 | 0.74 | 0.74 | 0.71 | 0.65 | 0.50 | 1.08 |
| co02 | 495 | 0.72 | 0.72 | 0.67 | 0.62 | 0.50 | 1.04 |
| co03 | 495 | 0.56 | 0.57 | 0.48 | 0.43 | 0.57 | 1.02 |
| co04 | 495 | 0.44 | 0.43 | 0.33 | 0.28 | 0.69 | 1.09 |
| co05 | 495 | 0.79 | 0.78 | 0.78 | 0.70 | 0.44 | 1.11 |
| co06 | 495 | 0.68 | 0.67 | 0.62 | 0.56 | 0.50 | 1.11 |
| co08 | 495 | 0.44 | 0.44 | 0.34 | 0.29 | 0.38 | 1.04 |
| co09 | 495 | 0.76 | 0.76 | 0.75 | 0.67 | 0.41 | 1.07 |
| co10 | 495 | 0.68 | 0.68 | 0.62 | 0.57 | 0.49 | 1.10 |
co2$item.stats %>%
ggplot(aes(x = row.names(co2$item.stats))) +
geom_point(aes(y = mean), color = "darkblue", size = 3) +
geom_point(aes(y = raw.r), color = "darkred", size = 2) +
geom_hline(yintercept = 0.1, color = "darkblue") +
geom_hline(yintercept = 0.9, color = "darkblue") +
geom_hline(yintercept = 0.25, color = "darkred") +
geom_hline(yintercept = 0, color = "black") +
labs(x = "Item", y = "Value",
title = "Consistency. Items characteristics",
subtitle = "Difficulty (blue) and Dicrimination (red)") +
theme(plot.title = element_text(hjust = .5),
plot.subtitle = element_text(hjust = .5))kable(co2$alpha.drop,
caption = "Consistency. Subscale statistics when item drop",
label = 13, digits = 2,
col.names = alpha_drop_colnames)| Alpha | Standardized Alpha | Guttman’s Lambda 6 | Average interitem correlation | S/N | Alpha SE | Var(r) | Median interitem correlation | |
|---|---|---|---|---|---|---|---|---|
| co01 | 0.79 | 0.79 | 0.80 | 0.32 | 3.81 | 0.01 | 0.03 | 0.34 |
| co02 | 0.80 | 0.80 | 0.81 | 0.33 | 3.89 | 0.01 | 0.03 | 0.35 |
| co03 | 0.82 | 0.82 | 0.82 | 0.36 | 4.49 | 0.01 | 0.03 | 0.41 |
| co04 | 0.84 | 0.83 | 0.83 | 0.39 | 5.04 | 0.01 | 0.02 | 0.41 |
| co05 | 0.79 | 0.79 | 0.79 | 0.31 | 3.66 | 0.01 | 0.02 | 0.33 |
| co06 | 0.80 | 0.80 | 0.81 | 0.34 | 4.06 | 0.01 | 0.03 | 0.35 |
| co08 | 0.83 | 0.83 | 0.83 | 0.39 | 5.01 | 0.01 | 0.02 | 0.40 |
| co09 | 0.79 | 0.79 | 0.80 | 0.32 | 3.74 | 0.01 | 0.02 | 0.34 |
| co10 | 0.80 | 0.80 | 0.81 | 0.34 | 4.04 | 0.01 | 0.03 | 0.35 |
kable(co2$response.freq,
caption = "Consistency. Non missing response frequency for each item",
label = 14, digits = 2)| 0 | 1 | 2 | 3 | 4 | 5 | miss | |
|---|---|---|---|---|---|---|---|
| co01 | 0.05 | 0.11 | 0.31 | 0.39 | 0.11 | 0.03 | 0 |
| co02 | 0.03 | 0.12 | 0.32 | 0.38 | 0.13 | 0.02 | 0 |
| co03 | 0.02 | 0.07 | 0.22 | 0.44 | 0.21 | 0.04 | 0 |
| co04 | 0.01 | 0.04 | 0.10 | 0.35 | 0.32 | 0.18 | 0 |
| co05 | 0.06 | 0.19 | 0.37 | 0.27 | 0.09 | 0.02 | 0 |
| co06 | 0.04 | 0.14 | 0.28 | 0.38 | 0.13 | 0.03 | 0 |
| co08 | 0.08 | 0.27 | 0.42 | 0.16 | 0.06 | 0.01 | 0 |
| co09 | 0.06 | 0.24 | 0.40 | 0.22 | 0.06 | 0.02 | 0 |
| co10 | 0.04 | 0.14 | 0.33 | 0.34 | 0.11 | 0.03 | 0 |
ut2 <- psych::alpha(
taia %>% select(all_of(ut_items_1)),
cumulative = TRUE,
title = "Utility Factor",
check.keys = FALSE
)kable(ut2$total,
caption = "Utility. Subscale statistics",
label = 15, digits = 2,
col.names = total_colnames)| Alpha | Standardized Alpha | Guttman’s Lambda 6 | Average interitem correlation | S/N | Alpha SE | Scale Mean | Total Score SD | Median interitem correlation | |
|---|---|---|---|---|---|---|---|---|---|
| 0.88 | 0.88 | 0.88 | 0.4 | 7.4 | 0.01 | 35.92 | 8.32 | 0.41 |
ut2$item.stats$mean <- ut2$item.stats$mean / 5
kable(ut2$item.stats,
caption = "Utility. Items statistics",
label = 16, digits = 2,
col.names = item_stats_colnames)| Num. of Obs. | Discrimination | Std Cor | Cor Overlap Corrected | Cor if drop | Difficulty | SD | |
|---|---|---|---|---|---|---|---|
| ut01 | 495 | 0.79 | 0.79 | 0.79 | 0.73 | 0.76 | 1.05 |
| ut02 | 495 | 0.83 | 0.84 | 0.84 | 0.79 | 0.70 | 1.05 |
| ut03 | 495 | 0.55 | 0.56 | 0.49 | 0.45 | 0.71 | 1.11 |
| ut04 | 495 | 0.53 | 0.53 | 0.46 | 0.42 | 0.62 | 1.11 |
| ut05 | 495 | 0.69 | 0.68 | 0.64 | 0.60 | 0.61 | 1.21 |
| ut06 | 495 | 0.77 | 0.77 | 0.75 | 0.71 | 0.65 | 1.10 |
| ut07 | 495 | 0.62 | 0.62 | 0.57 | 0.52 | 0.64 | 1.13 |
| ut08 | 495 | 0.66 | 0.67 | 0.62 | 0.58 | 0.69 | 1.05 |
| ut09 | 495 | 0.70 | 0.70 | 0.66 | 0.62 | 0.64 | 1.17 |
| ut11 | 495 | 0.57 | 0.56 | 0.49 | 0.46 | 0.53 | 1.23 |
| ut12 | 495 | 0.72 | 0.72 | 0.68 | 0.65 | 0.63 | 1.15 |
ut2$item.stats %>%
ggplot(aes(x = row.names(ut2$item.stats))) +
geom_point(aes(y = mean), color = "darkblue", size = 3) +
geom_point(aes(y = raw.r), color = "darkred", size = 2) +
geom_hline(yintercept = 0.1, color = "darkblue") +
geom_hline(yintercept = 0.9, color = "darkblue") +
geom_hline(yintercept = 0.25, color = "darkred") +
geom_hline(yintercept = 0, color = "black") +
labs(x = "Item", y = "Value",
title = "Utility. Items characteristics",
subtitle = "Difficulty (blue) and Dicrimination (red)") +
theme(plot.title = element_text(hjust = .5),
plot.subtitle = element_text(hjust = .5))kable(ut2$alpha.drop,
caption = "Utility. Subscale statistics when item drop",
label = 17, digits = 2,
col.names = alpha_drop_colnames)| Alpha | Standardized Alpha | Guttman’s Lambda 6 | Average interitem correlation | S/N | Alpha SE | Var(r) | Median interitem correlation | |
|---|---|---|---|---|---|---|---|---|
| ut01 | 0.86 | 0.86 | 0.86 | 0.38 | 6.21 | 0.01 | 0.01 | 0.40 |
| ut02 | 0.86 | 0.86 | 0.86 | 0.38 | 6.01 | 0.01 | 0.01 | 0.37 |
| ut03 | 0.88 | 0.88 | 0.88 | 0.42 | 7.30 | 0.01 | 0.01 | 0.43 |
| ut04 | 0.88 | 0.88 | 0.88 | 0.43 | 7.41 | 0.01 | 0.01 | 0.43 |
| ut05 | 0.87 | 0.87 | 0.87 | 0.40 | 6.69 | 0.01 | 0.01 | 0.41 |
| ut06 | 0.86 | 0.86 | 0.87 | 0.39 | 6.30 | 0.01 | 0.01 | 0.41 |
| ut07 | 0.87 | 0.87 | 0.87 | 0.41 | 6.99 | 0.01 | 0.01 | 0.42 |
| ut08 | 0.87 | 0.87 | 0.87 | 0.40 | 6.77 | 0.01 | 0.01 | 0.41 |
| ut09 | 0.87 | 0.87 | 0.87 | 0.40 | 6.64 | 0.01 | 0.02 | 0.41 |
| ut11 | 0.88 | 0.88 | 0.88 | 0.42 | 7.27 | 0.01 | 0.01 | 0.44 |
| ut12 | 0.86 | 0.87 | 0.87 | 0.39 | 6.52 | 0.01 | 0.02 | 0.41 |
kable(ut2$response.freq,
caption = "Utility. Non missing response frequency for each item",
label = 18, digits = 2)| 0 | 1 | 2 | 3 | 4 | 5 | miss | |
|---|---|---|---|---|---|---|---|
| ut01 | 0.01 | 0.01 | 0.06 | 0.29 | 0.34 | 0.28 | 0 |
| ut02 | 0.01 | 0.02 | 0.09 | 0.38 | 0.30 | 0.20 | 0 |
| ut03 | 0.01 | 0.04 | 0.09 | 0.33 | 0.31 | 0.22 | 0 |
| ut04 | 0.02 | 0.08 | 0.15 | 0.40 | 0.27 | 0.09 | 0 |
| ut05 | 0.03 | 0.06 | 0.20 | 0.35 | 0.23 | 0.12 | 0 |
| ut06 | 0.03 | 0.03 | 0.13 | 0.39 | 0.30 | 0.12 | 0 |
| ut07 | 0.01 | 0.05 | 0.19 | 0.35 | 0.26 | 0.14 | 0 |
| ut08 | 0.01 | 0.03 | 0.11 | 0.36 | 0.34 | 0.15 | 0 |
| ut09 | 0.03 | 0.05 | 0.15 | 0.38 | 0.25 | 0.13 | 0 |
| ut11 | 0.05 | 0.12 | 0.27 | 0.32 | 0.18 | 0.07 | 0 |
| ut12 | 0.02 | 0.07 | 0.15 | 0.38 | 0.26 | 0.12 | 0 |
de2 <- psych::alpha(
taia %>% select(all_of(de_items_1)),
cumulative = TRUE,
title = "Dependability Factor",
check.keys = FALSE
)kable(de2$total,
caption = "Dependability. Subscale statistics",
label = 23, digits = 2,
col.names = total_colnames)| Alpha | Standardized Alpha | Guttman’s Lambda 6 | Average interitem correlation | S/N | Alpha SE | Scale Mean | Total Score SD | Median interitem correlation | |
|---|---|---|---|---|---|---|---|---|---|
| 0.82 | 0.82 | 0.84 | 0.31 | 4.49 | 0.01 | 26.19 | 7.05 | 0.32 |
de2$item.stats$mean <- de2$item.stats$mean / 5
kable(de2$item.stats,
caption = "Dependability. Items statistics",
label = 24, digits = 2,
col.names = item_stats_colnames)| Num. of Obs. | Discrimination | Std Cor | Cor Overlap Corrected | Cor if drop | Difficulty | SD | |
|---|---|---|---|---|---|---|---|
| de01 | 495 | 0.58 | 0.59 | 0.53 | 0.47 | 0.52 | 1.10 |
| de02 | 495 | 0.72 | 0.72 | 0.68 | 0.62 | 0.43 | 1.15 |
| de03 | 495 | 0.65 | 0.65 | 0.60 | 0.54 | 0.43 | 1.19 |
| de05 | 495 | 0.62 | 0.62 | 0.59 | 0.50 | 0.71 | 1.16 |
| de06 | 495 | 0.67 | 0.67 | 0.62 | 0.56 | 0.45 | 1.23 |
| de07 | 495 | 0.61 | 0.62 | 0.56 | 0.51 | 0.56 | 1.00 |
| de08 | 495 | 0.70 | 0.71 | 0.67 | 0.61 | 0.53 | 1.06 |
| de09 | 495 | 0.46 | 0.45 | 0.40 | 0.31 | 0.69 | 1.20 |
| de10 | 495 | 0.73 | 0.73 | 0.71 | 0.64 | 0.45 | 1.18 |
| de11 | 495 | 0.41 | 0.40 | 0.28 | 0.25 | 0.46 | 1.20 |
de2$item.stats %>%
ggplot(aes(x = row.names(de2$item.stats))) +
geom_point(aes(y = mean), color = "darkblue", size = 3) +
geom_point(aes(y = raw.r), color = "darkred", size = 2) +
geom_hline(yintercept = 0.1, color = "darkblue") +
geom_hline(yintercept = 0.9, color = "darkblue") +
geom_hline(yintercept = 0.25, color = "darkred") +
geom_hline(yintercept = 0, color = "black") +
labs(x = "Item", y = "Value",
title = "Dependability. Items characteristics",
subtitle = "Difficulty (blue) and Dicrimination (red)") +
theme(plot.title = element_text(hjust = .5),
plot.subtitle = element_text(hjust = .5))kable(de2$alpha.drop,
caption = "Dependability. Subscale statistics when item drop",
label = 25, digits = 2,
col.names = alpha_drop_colnames)| Alpha | Standardized Alpha | Guttman’s Lambda 6 | Average interitem correlation | S/N | Alpha SE | Var(r) | Median interitem correlation | |
|---|---|---|---|---|---|---|---|---|
| de01 | 0.80 | 0.80 | 0.82 | 0.31 | 4.11 | 0.01 | 0.02 | 0.31 |
| de02 | 0.79 | 0.79 | 0.81 | 0.29 | 3.72 | 0.01 | 0.02 | 0.29 |
| de03 | 0.79 | 0.80 | 0.82 | 0.30 | 3.93 | 0.01 | 0.02 | 0.29 |
| de05 | 0.80 | 0.80 | 0.80 | 0.31 | 4.04 | 0.01 | 0.02 | 0.35 |
| de06 | 0.79 | 0.80 | 0.81 | 0.30 | 3.88 | 0.01 | 0.02 | 0.31 |
| de07 | 0.80 | 0.80 | 0.82 | 0.31 | 4.02 | 0.01 | 0.02 | 0.29 |
| de08 | 0.79 | 0.79 | 0.81 | 0.29 | 3.75 | 0.01 | 0.02 | 0.29 |
| de09 | 0.82 | 0.82 | 0.82 | 0.34 | 4.60 | 0.01 | 0.02 | 0.35 |
| de10 | 0.78 | 0.79 | 0.80 | 0.29 | 3.67 | 0.01 | 0.02 | 0.29 |
| de11 | 0.83 | 0.83 | 0.84 | 0.35 | 4.79 | 0.01 | 0.02 | 0.37 |
kable(de2$response.freq,
caption = "Dependability. Non missing response frequency for each item",
label = 26, digits = 2)| 0 | 1 | 2 | 3 | 4 | 5 | miss | |
|---|---|---|---|---|---|---|---|
| de01 | 0.05 | 0.11 | 0.24 | 0.43 | 0.14 | 0.03 | 0 |
| de02 | 0.09 | 0.17 | 0.36 | 0.26 | 0.10 | 0.02 | 0 |
| de03 | 0.10 | 0.17 | 0.35 | 0.27 | 0.09 | 0.03 | 0 |
| de05 | 0.02 | 0.03 | 0.10 | 0.28 | 0.34 | 0.23 | 0 |
| de06 | 0.10 | 0.17 | 0.32 | 0.28 | 0.11 | 0.03 | 0 |
| de07 | 0.02 | 0.06 | 0.27 | 0.42 | 0.20 | 0.03 | 0 |
| de08 | 0.04 | 0.10 | 0.24 | 0.44 | 0.15 | 0.03 | 0 |
| de09 | 0.01 | 0.06 | 0.13 | 0.27 | 0.33 | 0.20 | 0 |
| de10 | 0.10 | 0.15 | 0.30 | 0.34 | 0.10 | 0.02 | 0 |
| de11 | 0.07 | 0.20 | 0.28 | 0.31 | 0.12 | 0.03 | 0 |
splitHalf(taia %>% select(all_of(co_items_1)),
raw=F, brute=F, n.sample=100, covar=F,
check.keys=F, key=NULL, use="pairwise")## Split half reliabilities
## Call: splitHalf(r = taia %>% select(all_of(co_items_1)), raw = F, brute = F,
## n.sample = 100, covar = F, check.keys = F, key = NULL, use = "pairwise")
##
## Maximum split half reliability (lambda 4) = 0.87
## Guttman lambda 6 = 0.83
## Average split half reliability = 0.82
## Guttman lambda 3 (alpha) = 0.82
## Guttman lambda 2 = 0.83
## Minimum split half reliability (beta) = 0.72
## Average interitem r = 0.34 with median = 0.36
splitHalf(taia %>% select(all_of(ut_items_1)),
raw=F, brute=F, n.sample=100, covar=F,
check.keys=F, key=NULL, use="pairwise")## Split half reliabilities
## Call: splitHalf(r = taia %>% select(all_of(ut_items_1)), raw = F, brute = F,
## n.sample = 100, covar = F, check.keys = F, key = NULL, use = "pairwise")
##
## Maximum split half reliability (lambda 4) = 0.91
## Guttman lambda 6 = 0.88
## Average split half reliability = 0.87
## Guttman lambda 3 (alpha) = 0.88
## Guttman lambda 2 = 0.88
## Minimum split half reliability (beta) = 0.83
## Average interitem r = 0.4 with median = 0.41
splitHalf(taia %>% select(all_of(de_items_1)),
raw=F, brute=F, n.sample=100, covar=F,
check.keys=F, key=NULL, use="pairwise")## Split half reliabilities
## Call: splitHalf(r = taia %>% select(all_of(de_items_1)), raw = F, brute = F,
## n.sample = 100, covar = F, check.keys = F, key = NULL, use = "pairwise")
##
## Maximum split half reliability (lambda 4) = 0.88
## Guttman lambda 6 = 0.84
## Average split half reliability = 0.82
## Guttman lambda 3 (alpha) = 0.82
## Guttman lambda 2 = 0.83
## Minimum split half reliability (beta) = 0.72
## Average interitem r = 0.31 with median = 0.32
splitHalf(taia %>% ungroup() %>% select(all_of(taia_items_1)),
raw=F, brute=F, n.sample=100, covar=F,
check.keys=F, key=NULL, use="pairwise")## Split half reliabilities
## Call: splitHalf(r = taia %>% ungroup() %>% select(all_of(taia_items_1)),
## raw = F, brute = F, n.sample = 100, covar = F, check.keys = F,
## key = NULL, use = "pairwise")
##
## Maximum split half reliability (lambda 4) = 0.96
## Guttman lambda 6 = 0.97
## Average split half reliability = 0.94
## Guttman lambda 3 (alpha) = 0.94
## Guttman lambda 2 = 0.94
## Minimum split half reliability (beta) = 0.89
## Average interitem r = 0.2 with median = 0.2
efa_6f_vm <- factanal(
taia %>% select(all_of(taia_items_1)),
factors = 6,
scores = "regression",
rotation = "varimax"
)##
## Loadings:
## Factor1 Factor2 Factor3 Factor4 Factor5 Factor6
## pr01 0.489 0.475 0.172 0.287
## pr02 0.263 0.392 0.219 0.164 0.181
## pr03 0.219 0.547
## pr04 -0.109 0.139 0.437
## pr05 0.204 0.350 0.139 0.123 0.676
## pr06 0.475 0.279 0.144 0.105
## pr07 0.387 0.525 0.208 0.134
## pr08 0.492 0.429 0.125 0.241
## pr09 0.350 0.347 0.133 0.220
## pr10 0.328 0.301 0.124 0.108
## co01 0.243 0.676 0.116
## co02 0.169 0.626 0.115
## co03 0.416 0.370 0.159 0.141
## co04 0.532 0.147 0.121 0.161
## co05 0.137 0.724
## co06 0.152 0.554 -0.117
## co08 -0.118 0.413 -0.115 -0.127 -0.467
## co09 0.676 -0.160
## co10 0.184 0.526 0.206 -0.123
## ut01 0.810
## ut02 0.806 0.102 0.135 0.125
## ut03 0.472 -0.102 0.119 0.507
## ut04 0.461 0.104 0.132
## ut05 0.598 0.194
## ut06 0.717 0.164 0.125
## ut07 0.550 0.207
## ut08 0.576 0.249
## ut09 0.593 0.174 0.102 0.128
## ut11 0.368 0.255 0.158 0.126 0.591
## ut12 0.604 0.233 0.135 0.142
## fa01 0.300 0.345 0.628
## fa02 -0.207 0.669 0.148
## fa03 -0.116 0.385 -0.318 0.148
## fa04 0.549 0.103 -0.192 0.177
## fa05 0.293 0.411 0.622
## fa06 0.301 0.573 0.126 0.188 0.182 0.163
## fa07 0.196 0.474 0.193
## fa08 -0.191 0.602 0.232
## fa09 -0.155 0.631 0.310
## fa10 0.280 0.136 0.599
## de01 0.278 0.429 0.162
## de02 0.225 0.571 0.129 0.155 0.228
## de03 0.223 0.475 0.115 0.138 0.214
## de05 0.508 0.173 0.137 0.385
## de06 0.218 0.352 0.184 0.171 0.666
## de07 0.439 0.301 0.220 0.182
## de08 0.345 0.412 0.193 0.107 0.185 0.205
## de09 0.259 0.592
## de10 0.287 0.512 0.177 0.351
## de11 0.181 0.332 0.255
## un01 0.287 0.731
## un02 0.138 0.815
## un03 0.207 0.502 -0.247
## un04 0.113 0.711
## un05 0.191 0.791
## un06 -0.131 0.518 0.189
## un07 0.286 0.648 -0.139 0.128
## un08 0.182 0.746
## un09 0.162 0.662 0.197
## un10 0.261 0.690
## un11 0.763
## un12 0.236 0.713
##
## Factor1 Factor2 Factor3 Factor4 Factor5 Factor6
## SS loadings 7.605 7.077 6.534 2.832 2.786 2.029
## Proportion Var 0.123 0.114 0.105 0.046 0.045 0.033
## Cumulative Var 0.123 0.237 0.342 0.388 0.433 0.466
| Uniqueness | |
|---|---|
| de11 | 0.78 |
| pr04 | 0.77 |
| pr10 | 0.76 |
| ut04 | 0.76 |
| fa03 | 0.71 |
| de01 | 0.70 |
| fa07 | 0.69 |
| pr09 | 0.69 |
| un06 | 0.68 |
| pr02 | 0.67 |
| pr06 | 0.66 |
| ut07 | 0.65 |
| co04 | 0.65 |
| co06 | 0.65 |
| de03 | 0.65 |
| un03 | 0.64 |
| pr03 | 0.64 |
| co03 | 0.63 |
| co10 | 0.63 |
| de07 | 0.62 |
| fa04 | 0.61 |
| ut08 | 0.59 |
| ut05 | 0.59 |
| ut09 | 0.59 |
| de08 | 0.59 |
| de09 | 0.58 |
| co08 | 0.57 |
| co02 | 0.56 |
| de05 | 0.54 |
| fa10 | 0.54 |
| ut12 | 0.54 |
| fa08 | 0.53 |
| de02 | 0.52 |
| co09 | 0.51 |
| pr07 | 0.50 |
| pr08 | 0.50 |
| ut03 | 0.49 |
| de10 | 0.49 |
| un09 | 0.49 |
| fa09 | 0.47 |
| fa02 | 0.47 |
| fa06 | 0.47 |
| un04 | 0.46 |
| co01 | 0.46 |
| un07 | 0.46 |
| co05 | 0.45 |
| ut06 | 0.44 |
| un10 | 0.44 |
| un12 | 0.43 |
| pr01 | 0.41 |
| ut11 | 0.41 |
| un08 | 0.41 |
| un11 | 0.39 |
| fa01 | 0.38 |
| un01 | 0.37 |
| fa05 | 0.35 |
| pr05 | 0.34 |
| ut01 | 0.33 |
| un05 | 0.33 |
| de06 | 0.31 |
| un02 | 0.31 |
| ut02 | 0.30 |
efa_6f_pm <- factanal(
taia %>% select(all_of(taia_items_1)),
factors = 6,
scores = "regression",
rotation = "promax"
)##
## Loadings:
## Factor1 Factor2 Factor3 Factor4 Factor5 Factor6
## pr01 0.461 0.225 0.283
## pr02 0.338 0.110 0.148 0.136
## pr03 -0.110 0.614
## pr04 -0.141 0.484
## pr05 0.790
## pr06 0.174 0.410
## pr07 0.527 0.131 0.192
## pr08 0.422 0.278 0.238
## pr09 0.361 0.146 0.227
## pr10 0.263 0.204 -0.122
## co01 0.809 0.102 -0.145
## co02 0.765 -0.137
## co03 0.303 0.264 0.114
## co04 0.493 0.121 -0.132
## co05 0.902 -0.102 -0.117 -0.109
## co06 0.668 -0.154
## co08 0.542 -0.180 -0.501 -0.100
## co09 0.817 -0.113 -0.123 -0.169
## co10 0.598 0.115 -0.135
## ut01 -0.249 0.974
## ut02 -0.231 0.910 0.113
## ut03 -0.213 0.385 0.515
## ut04 -0.198 0.552 0.146
## ut05 0.658 -0.124
## ut06 0.789 0.108
## ut07 0.128 0.593
## ut08 0.186 0.543 -0.108
## ut09 0.608 0.110
## ut11 0.291 0.694
## ut12 0.106 0.559
## fa01 0.277 0.125 0.171 0.650
## fa02 -0.129 0.695
## fa03 0.418 -0.154 -0.379 0.158
## fa04 0.609 -0.225 -0.241 0.146
## fa05 0.367 0.105 0.156 -0.119 0.648
## fa06 0.580 0.149 0.131
## fa07 -0.218 0.508 0.109 0.173
## fa08 -0.179 0.165 0.604
## fa09 -0.187 0.254 0.627
## fa10 0.281 -0.127 0.648 -0.119
## de01 0.460
## de02 0.569 0.135 0.166
## de03 0.430 0.173
## de05 0.114 0.340 0.407 -0.107
## de06 0.770
## de07 0.160 0.109 0.337 0.156
## de08 0.329 0.129 0.141 0.153
## de09 0.692 -0.101
## de10 0.397 0.106 0.103 0.339
## de11 -0.106 -0.103 0.320 0.101 0.273
## un01 -0.148 0.804 0.225 0.123 -0.141
## un02 0.857 -0.105
## un03 0.451 0.145 -0.247
## un04 0.734
## un05 0.844
## un06 0.556 -0.347 0.256
## un07 0.192 0.641 -0.115 -0.172 0.113
## un08 -0.114 0.817 0.113
## un09 0.646 -0.101 0.212
## un10 0.219 0.698 -0.212
## un11 0.803 -0.103
## un12 -0.180 0.740 0.166
##
## Factor1 Factor2 Factor3 Factor4 Factor5 Factor6
## SS loadings 7.535 6.547 6.389 3.041 2.823 2.419
## Proportion Var 0.122 0.106 0.103 0.049 0.046 0.039
## Cumulative Var 0.122 0.227 0.330 0.379 0.425 0.464
| Uniqueness | |
|---|---|
| de11 | 0.78 |
| pr04 | 0.77 |
| pr10 | 0.76 |
| ut04 | 0.76 |
| fa03 | 0.71 |
| de01 | 0.70 |
| fa07 | 0.69 |
| pr09 | 0.69 |
| un06 | 0.68 |
| pr02 | 0.67 |
| pr06 | 0.66 |
| ut07 | 0.65 |
| co04 | 0.65 |
| co06 | 0.65 |
| de03 | 0.65 |
| un03 | 0.64 |
| pr03 | 0.64 |
| co03 | 0.63 |
| co10 | 0.63 |
| de07 | 0.62 |
| fa04 | 0.61 |
| ut08 | 0.59 |
| ut05 | 0.59 |
| ut09 | 0.59 |
| de08 | 0.59 |
| de09 | 0.58 |
| co08 | 0.57 |
| co02 | 0.56 |
| de05 | 0.54 |
| fa10 | 0.54 |
| ut12 | 0.54 |
| fa08 | 0.53 |
| de02 | 0.52 |
| co09 | 0.51 |
| pr07 | 0.50 |
| pr08 | 0.50 |
| ut03 | 0.49 |
| de10 | 0.49 |
| un09 | 0.49 |
| fa09 | 0.47 |
| fa02 | 0.47 |
| fa06 | 0.47 |
| un04 | 0.46 |
| co01 | 0.46 |
| un07 | 0.46 |
| co05 | 0.45 |
| ut06 | 0.44 |
| un10 | 0.44 |
| un12 | 0.43 |
| pr01 | 0.41 |
| ut11 | 0.41 |
| un08 | 0.41 |
| un11 | 0.39 |
| fa01 | 0.38 |
| un01 | 0.37 |
| fa05 | 0.35 |
| pr05 | 0.34 |
| ut01 | 0.33 |
| un05 | 0.33 |
| de06 | 0.31 |
| un02 | 0.31 |
| ut02 | 0.30 |
efa_5f_vm <- factanal(
taia %>% select(all_of(taia_items_1)),
factors = 5,
scores = "regression",
rotation = "varimax"
)##
## Loadings:
## Factor1 Factor2 Factor3 Factor4 Factor5
## pr01 0.576 0.372 0.170 0.212
## pr02 0.319 0.359 0.221 0.205
## pr03 0.306 -0.108 0.412 0.121
## pr04 0.131 -0.197 0.340 0.154
## pr05 0.211 0.453 0.156 0.482
## pr06 0.501 0.249 0.142 0.100
## pr07 0.456 0.465 0.232
## pr08 0.567 0.334 0.123 0.160
## pr09 0.424 0.257 0.129 0.129
## pr10 0.369 0.262 0.124 0.104
## co01 0.303 0.625 0.130
## co02 0.235 0.573 0.114 0.112
## co03 0.462 0.329 0.176
## co04 0.573 0.115
## co05 0.197 0.690
## co06 0.207 0.520
## co08 -0.157 0.505 -0.112 -0.326 -0.146
## co09 0.121 0.681 -0.100
## co10 0.219 0.513 0.206 -0.125
## ut01 0.788
## ut02 0.792 0.100 0.136
## ut03 0.531 -0.225 0.358 0.140
## ut04 0.437 0.115
## ut05 0.586 0.191
## ut06 0.713 0.148
## ut07 0.559 0.171
## ut08 0.612 0.179
## ut09 0.601 0.152 0.128
## ut11 0.362 0.341 0.171 0.431
## ut12 0.632 0.179 0.131 0.115
## fa01 0.317 0.338 0.123 0.618
## fa02 -0.206 0.202 0.659
## fa03 -0.148 0.484 -0.103
## fa04 0.616
## fa05 0.305 0.412 0.604
## fa06 0.366 0.528 0.127 0.241 0.179
## fa07 0.512 0.182
## fa08 -0.190 0.284 0.591
## fa09 -0.155 0.321 0.632
## fa10 0.278 0.126 0.611
## de01 0.320 0.386 0.159
## de02 0.282 0.553 0.132 0.281
## de03 0.246 0.493 0.117 0.176 0.105
## de05 0.583 0.133 0.230
## de06 0.219 0.462 0.197 0.470
## de07 0.462 0.290 0.220 0.182
## de08 0.392 0.386 0.195 0.273
## de09 0.367 0.374
## de10 0.300 0.558 0.303 0.121
## de11 0.445 0.153
## un01 0.301 0.727
## un02 0.124 0.815
## un03 0.221 0.501 -0.243
## un04 0.107 0.116 0.712
## un05 0.219 0.787
## un06 0.519 0.148
## un07 0.328 0.651
## un08 0.195 0.744
## un09 0.198 0.667 0.124 -0.110
## un10 0.272 0.692 -0.101
## un11 0.107 0.764
## un12 0.242 0.712
##
## Factor1 Factor2 Factor3 Factor4 Factor5
## SS loadings 8.695 6.882 6.542 2.836 2.744
## Proportion Var 0.140 0.111 0.106 0.046 0.044
## Cumulative Var 0.140 0.251 0.357 0.403 0.447
| Uniqueness | |
|---|---|
| pr04 | 0.80 |
| ut04 | 0.79 |
| de11 | 0.77 |
| pr10 | 0.76 |
| fa03 | 0.73 |
| pr09 | 0.72 |
| de09 | 0.71 |
| de01 | 0.71 |
| pr03 | 0.71 |
| un06 | 0.70 |
| fa07 | 0.69 |
| pr02 | 0.68 |
| co06 | 0.67 |
| pr06 | 0.66 |
| ut07 | 0.65 |
| co04 | 0.64 |
| de03 | 0.64 |
| un03 | 0.64 |
| co03 | 0.63 |
| co10 | 0.63 |
| de07 | 0.62 |
| ut05 | 0.61 |
| fa04 | 0.61 |
| ut09 | 0.60 |
| co02 | 0.59 |
| ut08 | 0.59 |
| de05 | 0.59 |
| co08 | 0.58 |
| de08 | 0.58 |
| ut12 | 0.54 |
| ut11 | 0.54 |
| fa10 | 0.53 |
| fa08 | 0.53 |
| pr08 | 0.53 |
| ut03 | 0.52 |
| de02 | 0.52 |
| co09 | 0.51 |
| pr07 | 0.51 |
| co01 | 0.49 |
| pr05 | 0.49 |
| de10 | 0.49 |
| un09 | 0.49 |
| fa06 | 0.48 |
| co05 | 0.47 |
| fa09 | 0.47 |
| fa02 | 0.47 |
| de06 | 0.47 |
| un04 | 0.46 |
| un07 | 0.46 |
| ut06 | 0.46 |
| pr01 | 0.45 |
| un10 | 0.44 |
| un12 | 0.43 |
| un08 | 0.40 |
| un11 | 0.40 |
| fa01 | 0.39 |
| ut01 | 0.37 |
| un01 | 0.37 |
| fa05 | 0.36 |
| ut02 | 0.34 |
| un05 | 0.33 |
| un02 | 0.31 |
efa_5f_pm <- factanal(
taia %>% select(all_of(taia_items_1)),
factors = 5,
scores = "regression",
rotation = "promax"
)##
## Loadings:
## Factor1 Factor2 Factor3 Factor4 Factor5
## pr01 0.446 0.279 0.163
## pr02 0.143 0.296 0.114 0.211
## pr03 0.233 -0.214 -0.117 0.424
## pr04 -0.293 0.354 0.107
## pr05 -0.115 0.392 0.579
## pr06 0.456 0.170
## pr07 0.281 0.412 0.218
## pr08 0.480 0.250 0.106
## pr09 0.342 0.189
## pr10 0.300 0.206 -0.112
## co01 0.146 0.650 0.122
## co02 0.599 0.111
## co03 0.354 0.265 0.139
## co04 0.624
## co05 0.759 -0.112
## co06 0.545 -0.122
## co08 -0.181 0.655 -0.178 -0.313 -0.109
## co09 0.763 -0.105 -0.100
## co10 0.114 0.531 0.122 -0.157
## ut01 0.932 -0.173 -0.161
## ut02 0.859 -0.129
## ut03 0.556 -0.375 0.303
## ut04 0.452
## ut05 0.610 0.111
## ut06 0.757
## ut07 0.631 0.114 -0.191
## ut08 0.655 -0.102
## ut09 0.605
## ut11 0.121 0.247 0.486
## ut12 0.625
## fa01 0.152 0.310 0.125 0.634
## fa02 -0.203 0.143 0.681
## fa03 -0.291 0.581
## fa04 -0.281 0.695
## fa05 0.129 0.400 0.620
## fa06 0.134 0.490 0.229 0.130
## fa07 -0.104 0.583 0.102
## fa08 -0.210 0.254 0.592
## fa09 -0.158 0.280 0.627
## fa10 0.247 0.110 -0.175 0.648
## de01 0.200 0.356
## de02 0.524 0.319
## de03 0.478 0.183
## de05 0.562 0.179
## de06 -0.118 0.396 0.550
## de07 0.346 0.199 0.104 0.156
## de08 0.192 0.310 0.272
## de09 0.305 -0.213 0.381
## de10 0.534 0.332
## de11 -0.110 0.509
## un01 0.258 -0.197 0.810 -0.169 0.132
## un02 0.857
## un03 0.185 0.450 -0.246
## un04 0.739
## un05 0.103 -0.121 0.847
## un06 -0.250 -0.119 0.556 0.200
## un07 -0.175 0.266 0.641
## un08 0.101 -0.128 0.827 -0.111 0.126
## un09 -0.166 0.647 0.170
## un10 -0.202 0.204 0.699
## un11 0.805
## un12 0.151 -0.155 0.746
##
## Factor1 Factor2 Factor3 Factor4 Factor5
## SS loadings 7.635 6.936 6.600 3.174 2.713
## Proportion Var 0.123 0.112 0.106 0.051 0.044
## Cumulative Var 0.123 0.235 0.341 0.393 0.436
| Uniqueness | |
|---|---|
| pr04 | 0.8036037 |
| ut04 | 0.7875168 |
| de11 | 0.7660851 |
| pr10 | 0.7648954 |
| fa03 | 0.7322846 |
| pr09 | 0.7157810 |
| de09 | 0.7138430 |
| de01 | 0.7115636 |
| pr03 | 0.7071547 |
| un06 | 0.6984492 |
| fa07 | 0.6934404 |
| pr02 | 0.6780781 |
| co06 | 0.6691958 |
| pr06 | 0.6557484 |
| ut07 | 0.6500545 |
| co04 | 0.6435044 |
| de03 | 0.6411034 |
| un03 | 0.6404520 |
| co03 | 0.6336224 |
| co10 | 0.6291329 |
| de07 | 0.6207641 |
| ut05 | 0.6108805 |
| fa04 | 0.6096628 |
| ut09 | 0.5950296 |
| co02 | 0.5895055 |
| ut08 | 0.5880908 |
| de05 | 0.5859234 |
| co08 | 0.5800904 |
| de08 | 0.5776240 |
| ut12 | 0.5381209 |
| ut11 | 0.5378917 |
| fa10 | 0.5312032 |
| fa08 | 0.5256412 |
| pr08 | 0.5255447 |
| ut03 | 0.5181711 |
| de02 | 0.5158859 |
| co09 | 0.5114014 |
| pr07 | 0.5096638 |
| co01 | 0.4937745 |
| pr05 | 0.4926627 |
| de10 | 0.4869821 |
| un09 | 0.4861097 |
| fa06 | 0.4806029 |
| co05 | 0.4747549 |
| fa09 | 0.4734086 |
| fa02 | 0.4713133 |
| de06 | 0.4691654 |
| un04 | 0.4646410 |
| un07 | 0.4595727 |
| ut06 | 0.4594009 |
| pr01 | 0.4496805 |
| un10 | 0.4361787 |
| un12 | 0.4325425 |
| un08 | 0.4008023 |
| un11 | 0.3971241 |
| fa01 | 0.3854741 |
| ut01 | 0.3691461 |
| un01 | 0.3671937 |
| fa05 | 0.3613714 |
| ut02 | 0.3407660 |
| un05 | 0.3303704 |
| un02 | 0.3106689 |
efa_4f_vm <- factanal(
taia %>% select(all_of(taia_items_1)),
factors = 4,
scores = "regression",
rotation = "varimax"
)##
## Loadings:
## Factor1 Factor2 Factor3 Factor4
## pr01 0.587 0.391 0.173 0.154
## pr02 0.338 0.367 0.225 0.105
## pr03 0.355 0.326
## pr04 0.172 -0.184 0.315
## pr05 0.275 0.444 0.169 0.249
## pr06 0.508 0.257 0.143
## pr07 0.470 0.479 0.161
## pr08 0.574 0.350 0.123
## pr09 0.428 0.271 0.129 0.115
## pr10 0.379 0.267 0.125
## co01 0.271 0.643
## co02 0.204 0.589 0.107
## co03 0.470 0.344 0.135
## co04 0.557 0.111
## co05 0.162 0.700
## co06 0.195 0.523 -0.117
## co08 -0.202 0.492 -0.128 -0.316
## co09 0.686
## co10 0.187 0.519 0.196 -0.125
## ut01 0.767
## ut02 0.795 0.102 0.101
## ut03 0.569 -0.204 0.312
## ut04 0.442 0.118
## ut05 0.572 0.207
## ut06 0.712 0.163
## ut07 0.530 0.188
## ut08 0.595 0.198
## ut09 0.609 0.166
## ut11 0.418 0.338 0.182 0.195
## ut12 0.634 0.196 0.132
## fa01 0.278 0.374 0.546
## fa02 -0.114 -0.197 0.674
## fa03 -0.169 0.479
## fa04 0.618
## fa05 0.263 0.446 0.521
## fa06 0.374 0.545 0.131 0.248
## fa07 0.158 0.427
## fa08 -0.178 0.669
## fa09 -0.141 0.719
## fa10 0.220 0.166 0.481
## de01 0.308 0.402 0.157
## de02 0.307 0.559 0.139 0.167
## de03 0.252 0.503 0.119 0.155
## de05 0.609 0.140 0.109
## de06 0.276 0.458 0.209 0.295
## de07 0.479 0.300 0.223
## de08 0.415 0.398 0.200 0.194
## de09 0.414 0.238
## de10 0.325 0.565 0.232
## de11 0.139 0.366
## un01 0.278 0.716
## un02 0.132 0.815
## un03 0.236 0.501 -0.225
## un04 0.105 0.121 0.710
## un05 0.211 0.785
## un06 0.527
## un07 0.328 0.648 -0.110
## un08 0.178 0.737
## un09 0.195 0.669
## un10 0.272 0.690 -0.127
## un11 0.110 0.762
## un12 0.244 0.712
##
## Factor1 Factor2 Factor3 Factor4
## SS loadings 8.889 7.151 6.515 3.709
## Proportion Var 0.143 0.115 0.105 0.060
## Cumulative Var 0.143 0.259 0.364 0.424
| Uniqueness | |
|---|---|
| de11 | 0.84 |
| pr04 | 0.83 |
| fa07 | 0.79 |
| ut04 | 0.79 |
| pr10 | 0.77 |
| de09 | 0.76 |
| pr03 | 0.76 |
| fa03 | 0.74 |
| un06 | 0.72 |
| pr09 | 0.71 |
| de01 | 0.71 |
| fa10 | 0.69 |
| pr02 | 0.69 |
| ut07 | 0.68 |
| co06 | 0.67 |
| co04 | 0.66 |
| pr06 | 0.65 |
| de03 | 0.65 |
| un03 | 0.64 |
| co10 | 0.64 |
| ut11 | 0.64 |
| pr05 | 0.64 |
| co03 | 0.63 |
| de07 | 0.63 |
| ut05 | 0.62 |
| fa04 | 0.61 |
| ut08 | 0.60 |
| co08 | 0.60 |
| co02 | 0.60 |
| ut09 | 0.59 |
| de05 | 0.59 |
| de08 | 0.59 |
| de06 | 0.58 |
| de02 | 0.55 |
| ut12 | 0.54 |
| ut03 | 0.54 |
| pr08 | 0.52 |
| de10 | 0.52 |
| pr07 | 0.52 |
| fa08 | 0.51 |
| co09 | 0.51 |
| un09 | 0.51 |
| co01 | 0.50 |
| fa02 | 0.49 |
| fa06 | 0.48 |
| fa01 | 0.48 |
| co05 | 0.48 |
| un04 | 0.46 |
| fa09 | 0.46 |
| ut06 | 0.46 |
| fa05 | 0.46 |
| un07 | 0.46 |
| pr01 | 0.45 |
| un10 | 0.43 |
| un12 | 0.43 |
| un08 | 0.42 |
| un01 | 0.41 |
| ut01 | 0.40 |
| un11 | 0.40 |
| ut02 | 0.35 |
| un05 | 0.34 |
| un02 | 0.31 |
efa_4f_pm <- factanal(
taia %>% select(all_of(taia_items_1)),
factors = 4,
scores = "regression",
rotation = "promax"
)##
## Loadings:
## Factor1 Factor2 Factor3 Factor4
## pr01 0.515 0.294
## pr02 0.217 0.309 0.150
## pr03 0.371 -0.195 0.265
## pr04 0.154 -0.280 0.308
## pr05 0.406 0.109 0.225
## pr06 0.516 0.174 -0.122
## pr07 0.359 0.431
## pr08 0.543 0.264
## pr09 0.378 0.198
## pr10 0.363 0.214 -0.107
## co01 0.667
## co02 0.614
## co03 0.411 0.279
## co04 0.611
## co05 0.774 -0.122
## co06 0.559 -0.170
## co08 -0.270 0.651 -0.216 -0.301
## co09 0.777 -0.122 -0.111
## co10 0.537 -0.169
## ut01 0.933 -0.176 -0.188
## ut02 0.916 -0.128
## ut03 0.673 -0.364 0.189
## ut04 0.489
## ut05 0.618 0.113 -0.120
## ut06 0.809 -0.146
## ut07 0.599 0.112 -0.103 -0.159
## ut08 0.656 0.103 -0.129
## ut09 0.672 -0.103
## ut11 0.306 0.261 0.104 0.124
## ut12 0.674
## fa01 0.321 0.549
## fa02 -0.314 -0.103 0.777
## fa03 -0.335 0.587
## fa04 -0.302 0.710
## fa05 0.410 0.525
## fa06 0.181 0.511 0.195
## fa07 0.437
## fa08 -0.273 0.764
## fa09 -0.219 0.803
## fa10 0.116 0.481
## de01 0.189 0.368
## de02 0.118 0.544 0.124
## de03 0.495 0.123
## de05 0.665
## de06 0.411 0.159 0.280
## de07 0.425 0.209 0.126
## de08 0.278 0.327 0.116 0.126
## de09 0.447 -0.196 0.156
## de10 0.131 0.553 0.188
## de11 0.372
## un01 0.179 -0.198 0.750
## un02 -0.107 0.869
## un03 0.243 0.480 -0.283
## un04 0.745
## un05 -0.122 0.829
## un06 -0.214 -0.110 0.611 0.106
## un07 -0.189 0.270 0.659
## un08 -0.130 0.788
## un09 -0.110 0.698
## un10 -0.225 0.207 0.718
## un11 -0.115 0.813
## un12 0.142 -0.159 0.744
##
## Factor1 Factor2 Factor3 Factor4
## SS loadings 9.044 7.246 6.725 3.937
## Proportion Var 0.146 0.117 0.108 0.064
## Cumulative Var 0.146 0.263 0.371 0.435
| Uniqueness | |
|---|---|
| de11 | 0.84 |
| pr04 | 0.83 |
| fa07 | 0.79 |
| ut04 | 0.79 |
| pr10 | 0.77 |
| de09 | 0.76 |
| pr03 | 0.76 |
| fa03 | 0.74 |
| un06 | 0.72 |
| pr09 | 0.71 |
| de01 | 0.71 |
| fa10 | 0.69 |
| pr02 | 0.69 |
| ut07 | 0.68 |
| co06 | 0.67 |
| co04 | 0.66 |
| pr06 | 0.65 |
| de03 | 0.65 |
| un03 | 0.64 |
| co10 | 0.64 |
| ut11 | 0.64 |
| pr05 | 0.64 |
| co03 | 0.63 |
| de07 | 0.63 |
| ut05 | 0.62 |
| fa04 | 0.61 |
| ut08 | 0.60 |
| co08 | 0.60 |
| co02 | 0.60 |
| ut09 | 0.59 |
| de05 | 0.59 |
| de08 | 0.59 |
| de06 | 0.58 |
| de02 | 0.55 |
| ut12 | 0.54 |
| ut03 | 0.54 |
| pr08 | 0.52 |
| de10 | 0.52 |
| pr07 | 0.52 |
| fa08 | 0.51 |
| co09 | 0.51 |
| un09 | 0.51 |
| co01 | 0.50 |
| fa02 | 0.49 |
| fa06 | 0.48 |
| fa01 | 0.48 |
| co05 | 0.48 |
| un04 | 0.46 |
| fa09 | 0.46 |
| ut06 | 0.46 |
| fa05 | 0.46 |
| un07 | 0.46 |
| pr01 | 0.45 |
| un10 | 0.43 |
| un12 | 0.43 |
| un08 | 0.42 |
| un01 | 0.41 |
| ut01 | 0.40 |
| un11 | 0.40 |
| ut02 | 0.35 |
| un05 | 0.34 |
| un02 | 0.31 |
Model:
mdl1 <- "
PR =~ pr01 + pr02 + pr03 + pr04 + pr05 + pr06 + pr07 + pr08 + pr09 + pr10
CO =~ co01 + co02 + co03 + co04 + co05 + co06 + co08 + co09 + co10
UT =~ ut01 + ut02 + ut03 + ut04 + ut05 + ut06 + ut07 + ut08 + ut09 + ut11 + ut12
FA =~ fa01 + fa02 + fa03 + fa04 + fa05 + fa06 + fa07 + fa08 + fa09 + fa10
DE =~ de01 + de02 + de03 + de05 + de06 + de07 + de08 + de09 + de10 + de11
UN =~ un01 + un02 + un03 + un04 + un05 + un06 + un07 + un08 + un09 + un10 + un11 + un12
"CFA model fitting:
## lavaan 0.6-8 ended normally after 62 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 139
##
## Number of observations 495
##
## Model Test User Model:
##
## Test statistic 6326.223
## Degrees of freedom 1814
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## PR =~
## pr01 1.000
## pr02 0.727 0.055 13.199 0.000
## pr03 0.386 0.060 6.384 0.000
## pr04 0.153 0.063 2.431 0.015
## pr05 0.870 0.068 12.721 0.000
## pr06 0.808 0.061 13.285 0.000
## pr07 1.040 0.061 17.095 0.000
## pr08 0.847 0.050 17.029 0.000
## pr09 0.714 0.054 13.167 0.000
## pr10 0.666 0.060 11.105 0.000
## CO =~
## co01 1.000
## co02 0.896 0.061 14.596 0.000
## co03 0.646 0.061 10.618 0.000
## co04 0.459 0.065 7.065 0.000
## co05 1.090 0.065 16.652 0.000
## co06 0.856 0.066 13.052 0.000
## co08 0.438 0.062 7.047 0.000
## co09 0.978 0.063 15.527 0.000
## co10 0.831 0.065 12.804 0.000
## UT =~
## ut01 1.000
## ut02 1.083 0.055 19.815 0.000
## ut03 0.682 0.062 11.048 0.000
## ut04 0.636 0.062 10.244 0.000
## ut05 0.965 0.066 14.724 0.000
## ut06 1.008 0.058 17.348 0.000
## ut07 0.790 0.062 12.676 0.000
## ut08 0.807 0.057 14.082 0.000
## ut09 0.945 0.063 14.930 0.000
## ut11 0.789 0.068 11.527 0.000
## ut12 0.974 0.062 15.791 0.000
## FA =~
## fa01 1.000
## fa02 0.535 0.060 8.869 0.000
## fa03 0.219 0.060 3.670 0.000
## fa04 0.424 0.056 7.575 0.000
## fa05 1.028 0.051 20.280 0.000
## fa06 0.716 0.053 13.558 0.000
## fa07 0.335 0.057 5.911 0.000
## fa08 0.487 0.058 8.366 0.000
## fa09 0.626 0.059 10.519 0.000
## fa10 0.785 0.059 13.357 0.000
## DE =~
## de01 1.000
## de02 1.315 0.113 11.688 0.000
## de03 1.183 0.111 10.668 0.000
## de05 0.981 0.103 9.509 0.000
## de06 1.276 0.116 11.019 0.000
## de07 0.979 0.093 10.586 0.000
## de08 1.185 0.102 11.579 0.000
## de09 0.604 0.098 6.189 0.000
## de10 1.377 0.116 11.853 0.000
## de11 0.476 0.096 4.958 0.000
## UN =~
## un01 1.000
## un02 1.215 0.064 18.860 0.000
## un03 0.817 0.068 11.959 0.000
## un04 1.025 0.062 16.485 0.000
## un05 1.142 0.063 18.233 0.000
## un06 0.783 0.072 10.830 0.000
## un07 1.040 0.068 15.336 0.000
## un08 1.120 0.066 16.897 0.000
## un09 1.090 0.071 15.404 0.000
## un10 1.050 0.066 15.883 0.000
## un11 1.192 0.069 17.382 0.000
## un12 1.065 0.064 16.524 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## PR ~~
## CO 0.417 0.043 9.702 0.000
## UT 0.479 0.045 10.634 0.000
## FA 0.402 0.044 9.066 0.000
## DE 0.423 0.045 9.495 0.000
## UN 0.232 0.034 6.779 0.000
## CO ~~
## UT 0.280 0.038 7.332 0.000
## FA 0.355 0.044 8.018 0.000
## DE 0.326 0.039 8.350 0.000
## UN 0.171 0.033 5.125 0.000
## UT ~~
## FA 0.331 0.043 7.706 0.000
## DE 0.341 0.040 8.621 0.000
## UN 0.187 0.034 5.560 0.000
## FA ~~
## DE 0.366 0.043 8.534 0.000
## UN 0.061 0.035 1.738 0.082
## DE ~~
## UN 0.186 0.029 6.327 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .pr01 0.366 0.028 13.060 0.000
## .pr02 0.619 0.041 14.916 0.000
## .pr03 0.936 0.060 15.582 0.000
## .pr04 1.071 0.068 15.711 0.000
## .pr05 0.977 0.065 14.993 0.000
## .pr06 0.751 0.050 14.902 0.000
## .pr07 0.581 0.042 13.928 0.000
## .pr08 0.390 0.028 13.953 0.000
## .pr09 0.601 0.040 14.921 0.000
## .pr10 0.805 0.053 15.208 0.000
## .co01 0.526 0.040 13.135 0.000
## .co02 0.573 0.041 13.824 0.000
## .co03 0.780 0.052 15.010 0.000
## .co04 1.045 0.068 15.460 0.000
## .co05 0.481 0.039 12.355 0.000
## .co06 0.763 0.053 14.429 0.000
## .co08 0.957 0.062 15.462 0.000
## .co09 0.536 0.040 13.296 0.000
## .co10 0.763 0.053 14.505 0.000
## .ut01 0.443 0.033 13.514 0.000
## .ut02 0.333 0.027 12.266 0.000
## .ut03 0.918 0.060 15.239 0.000
## .ut04 0.959 0.063 15.322 0.000
## .ut05 0.844 0.058 14.654 0.000
## .ut06 0.528 0.038 13.844 0.000
## .ut07 0.866 0.058 15.029 0.000
## .ut08 0.674 0.046 14.788 0.000
## .ut09 0.776 0.053 14.606 0.000
## .ut11 1.107 0.073 15.183 0.000
## .ut12 0.689 0.048 14.384 0.000
## .fa01 0.393 0.036 11.016 0.000
## .fa02 1.156 0.076 15.296 0.000
## .fa03 1.245 0.079 15.664 0.000
## .fa04 1.028 0.067 15.424 0.000
## .fa05 0.348 0.034 10.153 0.000
## .fa06 0.744 0.051 14.506 0.000
## .fa07 1.092 0.070 15.551 0.000
## .fa08 1.094 0.071 15.349 0.000
## .fa09 1.071 0.071 15.086 0.000
## .fa10 0.932 0.064 14.555 0.000
## .de01 0.822 0.055 14.942 0.000
## .de02 0.678 0.048 14.054 0.000
## .de03 0.895 0.061 14.713 0.000
## .de05 0.985 0.065 15.098 0.000
## .de06 0.891 0.061 14.538 0.000
## .de07 0.636 0.043 14.749 0.000
## .de08 0.583 0.041 14.151 0.000
## .de09 1.297 0.083 15.551 0.000
## .de10 0.678 0.049 13.889 0.000
## .de11 1.361 0.087 15.625 0.000
## .un01 0.500 0.035 14.388 0.000
## .un02 0.399 0.030 13.239 0.000
## .un03 0.962 0.063 15.267 0.000
## .un04 0.541 0.037 14.425 0.000
## .un05 0.425 0.031 13.665 0.000
## .un06 1.147 0.075 15.374 0.000
## .un07 0.729 0.049 14.737 0.000
## .un08 0.583 0.041 14.285 0.000
## .un09 0.789 0.054 14.721 0.000
## .un10 0.655 0.045 14.601 0.000
## .un11 0.583 0.041 14.094 0.000
## .un12 0.578 0.040 14.413 0.000
## PR 0.605 0.059 10.207 0.000
## CO 0.633 0.069 9.144 0.000
## UT 0.659 0.066 9.942 0.000
## FA 0.816 0.077 10.599 0.000
## DE 0.377 0.058 6.490 0.000
## UN 0.604 0.064 9.418 0.000
Fit measures:
tibble(
`Model 1` = c(
"Chi-Squared",
"DF",
"p",
"GFI",
"AGFI",
"CFI",
"TLI",
"SRMR",
"RMSEA"
),
Value = round(fitmeasures(
model1,
c(
"chisq",
"df",
"pvalue",
"gfi",
"agfi",
"cfi",
"tli",
"srmr",
"rmsea"
)
), 4)
) %>%
kable()| Model 1 | Value |
|---|---|
| Chi-Squared | 6326.2235 |
| DF | 1814.0000 |
| p | 0.0000 |
| GFI | 0.6381 |
| AGFI | 0.6104 |
| CFI | 0.7096 |
| TLI | 0.6973 |
| SRMR | 0.1010 |
| RMSEA | 0.0709 |
Standardized solution:
Loadings:
smodel1 %>%
filter(op == "=~") %>%
kable(
col.names = c(
"Factor",
"",
"Item",
"Loading",
"SE",
"z",
"p",
"CI lower bound",
"CI upper bound"
),
digits = 3
)| Factor | Item | Loading | SE | z | p | CI lower bound | CI upper bound | |
|---|---|---|---|---|---|---|---|---|
| PR | =~ | pr01 | 0.789 | 0.020 | 39.913 | 0.000 | 0.751 | 0.828 |
| PR | =~ | pr02 | 0.584 | 0.032 | 18.261 | 0.000 | 0.521 | 0.647 |
| PR | =~ | pr03 | 0.296 | 0.043 | 6.854 | 0.000 | 0.211 | 0.381 |
| PR | =~ | pr04 | 0.114 | 0.047 | 2.456 | 0.014 | 0.023 | 0.206 |
| PR | =~ | pr05 | 0.565 | 0.033 | 17.164 | 0.000 | 0.501 | 0.630 |
| PR | =~ | pr06 | 0.587 | 0.032 | 18.467 | 0.000 | 0.525 | 0.650 |
| PR | =~ | pr07 | 0.728 | 0.024 | 30.723 | 0.000 | 0.682 | 0.775 |
| PR | =~ | pr08 | 0.726 | 0.024 | 30.439 | 0.000 | 0.679 | 0.773 |
| PR | =~ | pr09 | 0.583 | 0.032 | 18.186 | 0.000 | 0.520 | 0.645 |
| PR | =~ | pr10 | 0.500 | 0.036 | 13.888 | 0.000 | 0.429 | 0.570 |
| CO | =~ | co01 | 0.739 | 0.024 | 30.666 | 0.000 | 0.692 | 0.786 |
| CO | =~ | co02 | 0.686 | 0.027 | 25.127 | 0.000 | 0.632 | 0.739 |
| CO | =~ | co03 | 0.503 | 0.037 | 13.698 | 0.000 | 0.431 | 0.575 |
| CO | =~ | co04 | 0.337 | 0.043 | 7.853 | 0.000 | 0.253 | 0.421 |
| CO | =~ | co05 | 0.781 | 0.022 | 36.274 | 0.000 | 0.739 | 0.823 |
| CO | =~ | co06 | 0.615 | 0.031 | 19.688 | 0.000 | 0.554 | 0.676 |
| CO | =~ | co08 | 0.336 | 0.043 | 7.829 | 0.000 | 0.252 | 0.420 |
| CO | =~ | co09 | 0.728 | 0.025 | 29.429 | 0.000 | 0.680 | 0.777 |
| CO | =~ | co10 | 0.604 | 0.032 | 18.958 | 0.000 | 0.541 | 0.666 |
| UT | =~ | ut01 | 0.773 | 0.021 | 37.597 | 0.000 | 0.733 | 0.814 |
| UT | =~ | ut02 | 0.836 | 0.016 | 51.142 | 0.000 | 0.804 | 0.868 |
| UT | =~ | ut03 | 0.500 | 0.036 | 13.954 | 0.000 | 0.430 | 0.570 |
| UT | =~ | ut04 | 0.466 | 0.037 | 12.493 | 0.000 | 0.393 | 0.539 |
| UT | =~ | ut05 | 0.649 | 0.028 | 22.945 | 0.000 | 0.593 | 0.704 |
| UT | =~ | ut06 | 0.748 | 0.022 | 33.612 | 0.000 | 0.704 | 0.791 |
| UT | =~ | ut07 | 0.568 | 0.033 | 17.378 | 0.000 | 0.504 | 0.632 |
| UT | =~ | ut08 | 0.624 | 0.030 | 21.010 | 0.000 | 0.566 | 0.682 |
| UT | =~ | ut09 | 0.657 | 0.028 | 23.611 | 0.000 | 0.602 | 0.711 |
| UT | =~ | ut11 | 0.520 | 0.035 | 14.890 | 0.000 | 0.452 | 0.589 |
| UT | =~ | ut12 | 0.690 | 0.026 | 26.662 | 0.000 | 0.639 | 0.741 |
| FA | =~ | fa01 | 0.822 | 0.019 | 42.299 | 0.000 | 0.784 | 0.860 |
| FA | =~ | fa02 | 0.410 | 0.040 | 10.123 | 0.000 | 0.330 | 0.489 |
| FA | =~ | fa03 | 0.175 | 0.047 | 3.752 | 0.000 | 0.083 | 0.266 |
| FA | =~ | fa04 | 0.353 | 0.042 | 8.337 | 0.000 | 0.270 | 0.436 |
| FA | =~ | fa05 | 0.844 | 0.018 | 46.444 | 0.000 | 0.809 | 0.880 |
| FA | =~ | fa06 | 0.600 | 0.032 | 18.680 | 0.000 | 0.537 | 0.663 |
| FA | =~ | fa07 | 0.279 | 0.044 | 6.264 | 0.000 | 0.191 | 0.366 |
| FA | =~ | fa08 | 0.388 | 0.041 | 9.407 | 0.000 | 0.307 | 0.469 |
| FA | =~ | fa09 | 0.479 | 0.038 | 12.693 | 0.000 | 0.405 | 0.553 |
| FA | =~ | fa10 | 0.592 | 0.033 | 18.218 | 0.000 | 0.528 | 0.656 |
| DE | =~ | de01 | 0.561 | 0.033 | 16.808 | 0.000 | 0.495 | 0.626 |
| DE | =~ | de02 | 0.700 | 0.026 | 27.265 | 0.000 | 0.650 | 0.751 |
| DE | =~ | de03 | 0.609 | 0.031 | 19.717 | 0.000 | 0.548 | 0.670 |
| DE | =~ | de05 | 0.519 | 0.035 | 14.677 | 0.000 | 0.450 | 0.588 |
| DE | =~ | de06 | 0.639 | 0.029 | 21.837 | 0.000 | 0.582 | 0.696 |
| DE | =~ | de07 | 0.602 | 0.031 | 19.273 | 0.000 | 0.541 | 0.663 |
| DE | =~ | de08 | 0.690 | 0.026 | 26.215 | 0.000 | 0.638 | 0.741 |
| DE | =~ | de09 | 0.310 | 0.043 | 7.191 | 0.000 | 0.225 | 0.394 |
| DE | =~ | de10 | 0.716 | 0.025 | 29.002 | 0.000 | 0.668 | 0.765 |
| DE | =~ | de11 | 0.243 | 0.045 | 5.436 | 0.000 | 0.155 | 0.331 |
| UN | =~ | un01 | 0.740 | 0.022 | 33.462 | 0.000 | 0.696 | 0.783 |
| UN | =~ | un02 | 0.831 | 0.016 | 52.491 | 0.000 | 0.800 | 0.862 |
| UN | =~ | un03 | 0.544 | 0.033 | 16.332 | 0.000 | 0.478 | 0.609 |
| UN | =~ | un04 | 0.735 | 0.022 | 32.798 | 0.000 | 0.691 | 0.779 |
| UN | =~ | un05 | 0.806 | 0.018 | 45.760 | 0.000 | 0.771 | 0.840 |
| UN | =~ | un06 | 0.494 | 0.036 | 13.895 | 0.000 | 0.425 | 0.564 |
| UN | =~ | un07 | 0.687 | 0.025 | 27.059 | 0.000 | 0.638 | 0.737 |
| UN | =~ | un08 | 0.752 | 0.021 | 35.289 | 0.000 | 0.710 | 0.794 |
| UN | =~ | un09 | 0.690 | 0.025 | 27.358 | 0.000 | 0.641 | 0.740 |
| UN | =~ | un10 | 0.710 | 0.024 | 29.592 | 0.000 | 0.663 | 0.757 |
| UN | =~ | un11 | 0.772 | 0.020 | 38.620 | 0.000 | 0.732 | 0.811 |
| UN | =~ | un12 | 0.737 | 0.022 | 33.023 | 0.000 | 0.693 | 0.780 |
Covariances:
smodel1 %>%
filter(op == "~~" & lhs != rhs) %>%
kable(
col.names = c(
"Factor",
"",
"Factor",
"Covariance",
"SE",
"z",
"p",
"CI lower bound",
"CI upper bound"
),
digits = 3
)| Factor | Factor | Covariance | SE | z | p | CI lower bound | CI upper bound | |
|---|---|---|---|---|---|---|---|---|
| PR | ~~ | CO | 0.674 | 0.032 | 21.123 | 0.000 | 0.611 | 0.736 |
| PR | ~~ | UT | 0.758 | 0.025 | 29.745 | 0.000 | 0.708 | 0.808 |
| PR | ~~ | FA | 0.572 | 0.037 | 15.251 | 0.000 | 0.498 | 0.645 |
| PR | ~~ | DE | 0.886 | 0.019 | 47.788 | 0.000 | 0.849 | 0.922 |
| PR | ~~ | UN | 0.383 | 0.043 | 8.811 | 0.000 | 0.298 | 0.469 |
| CO | ~~ | UT | 0.434 | 0.042 | 10.255 | 0.000 | 0.351 | 0.517 |
| CO | ~~ | FA | 0.493 | 0.041 | 12.022 | 0.000 | 0.413 | 0.574 |
| CO | ~~ | DE | 0.667 | 0.033 | 20.416 | 0.000 | 0.603 | 0.731 |
| CO | ~~ | UN | 0.276 | 0.047 | 5.929 | 0.000 | 0.185 | 0.367 |
| UT | ~~ | FA | 0.451 | 0.042 | 10.813 | 0.000 | 0.369 | 0.532 |
| UT | ~~ | DE | 0.684 | 0.030 | 22.435 | 0.000 | 0.624 | 0.744 |
| UT | ~~ | UN | 0.296 | 0.045 | 6.570 | 0.000 | 0.207 | 0.384 |
| FA | ~~ | DE | 0.659 | 0.033 | 19.845 | 0.000 | 0.594 | 0.724 |
| FA | ~~ | UN | 0.087 | 0.050 | 1.762 | 0.078 | -0.010 | 0.185 |
| DE | ~~ | UN | 0.389 | 0.044 | 8.920 | 0.000 | 0.304 | 0.475 |
Residuals:
smodel1 %>%
filter(op == "~~" & lhs == rhs) %>%
select(-(2:3)) %>%
kable(
col.names = c(
"Item",
"Residual",
"SE",
"z",
"p",
"CI lower bound",
"CI upper bound"
),
digits = 3
)| Item | Residual | SE | z | p | CI lower bound | CI upper bound |
|---|---|---|---|---|---|---|
| pr01 | 0.377 | 0.031 | 12.076 | 0 | 0.316 | 0.438 |
| pr02 | 0.659 | 0.037 | 17.653 | 0 | 0.586 | 0.732 |
| pr03 | 0.912 | 0.026 | 35.640 | 0 | 0.862 | 0.962 |
| pr04 | 0.987 | 0.011 | 92.749 | 0 | 0.966 | 1.008 |
| pr05 | 0.681 | 0.037 | 18.291 | 0 | 0.608 | 0.754 |
| pr06 | 0.655 | 0.037 | 17.542 | 0 | 0.582 | 0.728 |
| pr07 | 0.470 | 0.035 | 13.609 | 0 | 0.402 | 0.537 |
| pr08 | 0.473 | 0.035 | 13.667 | 0 | 0.405 | 0.541 |
| pr09 | 0.661 | 0.037 | 17.694 | 0 | 0.587 | 0.734 |
| pr10 | 0.750 | 0.036 | 20.839 | 0 | 0.680 | 0.821 |
| co01 | 0.454 | 0.036 | 12.730 | 0 | 0.384 | 0.523 |
| co02 | 0.530 | 0.037 | 14.153 | 0 | 0.456 | 0.603 |
| co03 | 0.747 | 0.037 | 20.238 | 0 | 0.675 | 0.820 |
| co04 | 0.887 | 0.029 | 30.728 | 0 | 0.830 | 0.943 |
| co05 | 0.390 | 0.034 | 11.593 | 0 | 0.324 | 0.456 |
| co06 | 0.622 | 0.038 | 16.181 | 0 | 0.546 | 0.697 |
| co08 | 0.887 | 0.029 | 30.806 | 0 | 0.831 | 0.944 |
| co09 | 0.469 | 0.036 | 13.014 | 0 | 0.399 | 0.540 |
| co10 | 0.636 | 0.038 | 16.536 | 0 | 0.560 | 0.711 |
| ut01 | 0.402 | 0.032 | 12.630 | 0 | 0.340 | 0.464 |
| ut02 | 0.301 | 0.027 | 11.006 | 0 | 0.247 | 0.354 |
| ut03 | 0.750 | 0.036 | 20.907 | 0 | 0.679 | 0.820 |
| ut04 | 0.783 | 0.035 | 22.500 | 0 | 0.715 | 0.851 |
| ut05 | 0.579 | 0.037 | 15.774 | 0 | 0.507 | 0.651 |
| ut06 | 0.441 | 0.033 | 13.251 | 0 | 0.376 | 0.506 |
| ut07 | 0.678 | 0.037 | 18.290 | 0 | 0.605 | 0.751 |
| ut08 | 0.611 | 0.037 | 16.492 | 0 | 0.538 | 0.683 |
| ut09 | 0.568 | 0.037 | 15.554 | 0 | 0.497 | 0.640 |
| ut11 | 0.729 | 0.036 | 20.063 | 0 | 0.658 | 0.801 |
| ut12 | 0.524 | 0.036 | 14.683 | 0 | 0.454 | 0.594 |
| fa01 | 0.325 | 0.032 | 10.177 | 0 | 0.262 | 0.387 |
| fa02 | 0.832 | 0.033 | 25.070 | 0 | 0.767 | 0.897 |
| fa03 | 0.969 | 0.016 | 59.581 | 0 | 0.938 | 1.001 |
| fa04 | 0.875 | 0.030 | 29.210 | 0 | 0.816 | 0.934 |
| fa05 | 0.287 | 0.031 | 9.357 | 0 | 0.227 | 0.347 |
| fa06 | 0.640 | 0.039 | 16.624 | 0 | 0.565 | 0.716 |
| fa07 | 0.922 | 0.025 | 37.218 | 0 | 0.874 | 0.971 |
| fa08 | 0.849 | 0.032 | 26.527 | 0 | 0.787 | 0.912 |
| fa09 | 0.770 | 0.036 | 21.268 | 0 | 0.699 | 0.841 |
| fa10 | 0.649 | 0.038 | 16.872 | 0 | 0.574 | 0.725 |
| de01 | 0.686 | 0.037 | 18.318 | 0 | 0.612 | 0.759 |
| de02 | 0.510 | 0.036 | 14.162 | 0 | 0.439 | 0.580 |
| de03 | 0.629 | 0.038 | 16.722 | 0 | 0.555 | 0.703 |
| de05 | 0.731 | 0.037 | 19.910 | 0 | 0.659 | 0.803 |
| de06 | 0.592 | 0.037 | 15.832 | 0 | 0.519 | 0.665 |
| de07 | 0.637 | 0.038 | 16.934 | 0 | 0.564 | 0.711 |
| de08 | 0.524 | 0.036 | 14.436 | 0 | 0.453 | 0.595 |
| de09 | 0.904 | 0.027 | 33.919 | 0 | 0.852 | 0.956 |
| de10 | 0.487 | 0.035 | 13.746 | 0 | 0.417 | 0.556 |
| de11 | 0.941 | 0.022 | 43.330 | 0 | 0.898 | 0.984 |
| un01 | 0.453 | 0.033 | 13.854 | 0 | 0.389 | 0.517 |
| un02 | 0.309 | 0.026 | 11.747 | 0 | 0.258 | 0.361 |
| un03 | 0.705 | 0.036 | 19.471 | 0 | 0.634 | 0.775 |
| un04 | 0.460 | 0.033 | 13.962 | 0 | 0.395 | 0.524 |
| un05 | 0.350 | 0.028 | 12.343 | 0 | 0.295 | 0.406 |
| un06 | 0.756 | 0.035 | 21.495 | 0 | 0.687 | 0.825 |
| un07 | 0.528 | 0.035 | 15.106 | 0 | 0.459 | 0.596 |
| un08 | 0.435 | 0.032 | 13.574 | 0 | 0.372 | 0.498 |
| un09 | 0.524 | 0.035 | 15.035 | 0 | 0.455 | 0.592 |
| un10 | 0.496 | 0.034 | 14.549 | 0 | 0.429 | 0.563 |
| un11 | 0.405 | 0.031 | 13.125 | 0 | 0.344 | 0.465 |
| un12 | 0.458 | 0.033 | 13.925 | 0 | 0.393 | 0.522 |
| PR | 1.000 | 0.000 | NA | NA | 1.000 | 1.000 |
| CO | 1.000 | 0.000 | NA | NA | 1.000 | 1.000 |
| UT | 1.000 | 0.000 | NA | NA | 1.000 | 1.000 |
| FA | 1.000 | 0.000 | NA | NA | 1.000 | 1.000 |
| DE | 1.000 | 0.000 | NA | NA | 1.000 | 1.000 |
| UN | 1.000 | 0.000 | NA | NA | 1.000 | 1.000 |
Visualization:
semPaths(model1,
what = "std",
whatLabels = "est",
style = "lisrel",
residScale = 10,
theme = "colorblind",
rotation = 1,
layout = "tree",
cardinal = "lat cov",
curvePivot = TRUE,
sizeMan = 3,
sizeLat = 7)Model:
mdl2 <- "
PR =~ pr01 + pr02 + pr03 + pr04 + pr05 + pr06 + pr07 + pr08 + pr09 + pr10
CO =~ co01 + co02 + co03 + co04 + co05 + co06 + co08 + co09 + co10
UT =~ ut01 + ut02 + ut03 + ut04 + ut05 + ut06 + ut07 + ut08 + ut09 + ut11 + ut12
FA =~ fa01 + fa02 + fa03 + fa04 + fa05 + fa06 + fa07 + fa08 + fa09 + fa10
DE =~ de01 + de02 + de03 + de05 + de06 + de07 + de08 + de09 + de10 + de11
UN =~ un01 + un02 + un03 + un04 + un05 + un06 + un07 + un08 + un09 + un10 + un11 + un12
DT =~ PR + CO + UT + FA + DE + UN
"## lavaan 0.6-8 ended normally after 44 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 130
##
## Number of observations 495
##
## Model Test User Model:
##
## Test statistic 6381.193
## Degrees of freedom 1823
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## PR =~
## pr01 1.000
## pr02 0.732 0.055 13.227 0.000
## pr03 0.389 0.061 6.409 0.000
## pr04 0.160 0.063 2.533 0.011
## pr05 0.882 0.069 12.845 0.000
## pr06 0.797 0.061 13.006 0.000
## pr07 1.049 0.061 17.137 0.000
## pr08 0.846 0.050 16.877 0.000
## pr09 0.719 0.055 13.186 0.000
## pr10 0.661 0.060 10.962 0.000
## CO =~
## co01 1.000
## co02 0.894 0.062 14.491 0.000
## co03 0.652 0.061 10.699 0.000
## co04 0.473 0.065 7.263 0.000
## co05 1.088 0.066 16.548 0.000
## co06 0.862 0.066 13.089 0.000
## co08 0.435 0.062 6.976 0.000
## co09 0.977 0.063 15.442 0.000
## co10 0.834 0.065 12.798 0.000
## UT =~
## ut01 1.000
## ut02 1.080 0.055 19.757 0.000
## ut03 0.674 0.062 10.910 0.000
## ut04 0.635 0.062 10.237 0.000
## ut05 0.972 0.065 14.837 0.000
## ut06 1.007 0.058 17.336 0.000
## ut07 0.791 0.062 12.699 0.000
## ut08 0.807 0.057 14.089 0.000
## ut09 0.946 0.063 14.941 0.000
## ut11 0.790 0.068 11.534 0.000
## ut12 0.973 0.062 15.774 0.000
## FA =~
## fa01 1.000
## fa02 0.522 0.060 8.760 0.000
## fa03 0.204 0.059 3.452 0.001
## fa04 0.407 0.055 7.352 0.000
## fa05 1.017 0.050 20.447 0.000
## fa06 0.704 0.052 13.544 0.000
## fa07 0.323 0.056 5.749 0.000
## fa08 0.475 0.058 8.239 0.000
## fa09 0.616 0.059 10.488 0.000
## fa10 0.780 0.058 13.496 0.000
## DE =~
## de01 1.000
## de02 1.314 0.113 11.628 0.000
## de03 1.176 0.111 10.576 0.000
## de05 1.006 0.104 9.645 0.000
## de06 1.267 0.116 10.914 0.000
## de07 0.989 0.093 10.609 0.000
## de08 1.188 0.103 11.539 0.000
## de09 0.623 0.098 6.342 0.000
## de10 1.371 0.117 11.764 0.000
## de11 0.475 0.096 4.934 0.000
## UN =~
## un01 1.000
## un02 1.212 0.064 18.935 0.000
## un03 0.811 0.068 11.917 0.000
## un04 1.022 0.062 16.530 0.000
## un05 1.140 0.062 18.326 0.000
## un06 0.780 0.072 10.831 0.000
## un07 1.035 0.067 15.346 0.000
## un08 1.118 0.066 16.974 0.000
## un09 1.085 0.070 15.412 0.000
## un10 1.046 0.066 15.902 0.000
## un11 1.188 0.068 17.430 0.000
## un12 1.061 0.064 16.562 0.000
## DT =~
## PR 1.000
## CO 0.746 0.061 12.327 0.000
## UT 0.818 0.060 13.693 0.000
## FA 0.781 0.064 12.133 0.000
## DE 0.777 0.066 11.697 0.000
## UN 0.408 0.053 7.651 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .pr01 0.369 0.028 13.031 0.000
## .pr02 0.616 0.041 14.883 0.000
## .pr03 0.935 0.060 15.576 0.000
## .pr04 1.070 0.068 15.709 0.000
## .pr05 0.966 0.065 14.947 0.000
## .pr06 0.763 0.051 14.921 0.000
## .pr07 0.573 0.041 13.836 0.000
## .pr08 0.393 0.028 13.938 0.000
## .pr09 0.599 0.040 14.890 0.000
## .pr10 0.810 0.053 15.208 0.000
## .co01 0.528 0.040 13.119 0.000
## .co02 0.578 0.042 13.832 0.000
## .co03 0.775 0.052 14.982 0.000
## .co04 1.037 0.067 15.438 0.000
## .co05 0.485 0.039 12.362 0.000
## .co06 0.759 0.053 14.391 0.000
## .co08 0.959 0.062 15.463 0.000
## .co09 0.539 0.041 13.290 0.000
## .co10 0.761 0.053 14.482 0.000
## .ut01 0.442 0.033 13.486 0.000
## .ut02 0.336 0.027 12.276 0.000
## .ut03 0.925 0.061 15.248 0.000
## .ut04 0.959 0.063 15.318 0.000
## .ut05 0.836 0.057 14.615 0.000
## .ut06 0.528 0.038 13.826 0.000
## .ut07 0.864 0.058 15.017 0.000
## .ut08 0.673 0.046 14.776 0.000
## .ut09 0.775 0.053 14.590 0.000
## .ut11 1.106 0.073 15.176 0.000
## .ut12 0.690 0.048 14.373 0.000
## .fa01 0.375 0.035 10.619 0.000
## .fa02 1.162 0.076 15.307 0.000
## .fa03 1.250 0.080 15.672 0.000
## .fa04 1.036 0.067 15.443 0.000
## .fa05 0.348 0.035 10.076 0.000
## .fa06 0.748 0.052 14.517 0.000
## .fa07 1.097 0.070 15.560 0.000
## .fa08 1.100 0.072 15.361 0.000
## .fa09 1.074 0.071 15.090 0.000
## .fa10 0.927 0.064 14.529 0.000
## .de01 0.823 0.055 14.925 0.000
## .de02 0.681 0.049 14.028 0.000
## .de03 0.904 0.061 14.713 0.000
## .de05 0.968 0.064 15.039 0.000
## .de06 0.902 0.062 14.545 0.000
## .de07 0.630 0.043 14.698 0.000
## .de08 0.583 0.041 14.109 0.000
## .de09 1.288 0.083 15.533 0.000
## .de10 0.687 0.049 13.891 0.000
## .de11 1.361 0.087 15.623 0.000
## .un01 0.497 0.035 14.367 0.000
## .un02 0.399 0.030 13.228 0.000
## .un03 0.966 0.063 15.272 0.000
## .un04 0.541 0.038 14.422 0.000
## .un05 0.423 0.031 13.641 0.000
## .un06 1.148 0.075 15.374 0.000
## .un07 0.732 0.050 14.740 0.000
## .un08 0.581 0.041 14.271 0.000
## .un09 0.792 0.054 14.725 0.000
## .un10 0.657 0.045 14.604 0.000
## .un11 0.584 0.041 14.091 0.000
## .un12 0.578 0.040 14.412 0.000
## .PR 0.051 0.017 2.954 0.003
## .CO 0.324 0.039 8.231 0.000
## .UT 0.290 0.033 8.734 0.000
## .FA 0.498 0.051 9.744 0.000
## .DE 0.043 0.012 3.438 0.001
## .UN 0.516 0.055 9.336 0.000
## DT 0.552 0.058 9.531 0.000
tibble(
`Model 2` = c(
"Chi-Squared",
"DF",
"p",
"GFI",
"AGFI",
"CFI",
"TLI",
"SRMR",
"RMSEA"
),
Value = round(fitmeasures(
model2,
c(
"chisq",
"df",
"pvalue",
"gfi",
"agfi",
"cfi",
"tli",
"srmr",
"rmsea"
)
), 4)
) %>% kable()| Model 2 | Value |
|---|---|
| Chi-Squared | 6381.1932 |
| DF | 1823.0000 |
| p | 0.0000 |
| GFI | 0.6329 |
| AGFI | 0.6068 |
| CFI | 0.7067 |
| TLI | 0.6957 |
| SRMR | 0.1029 |
| RMSEA | 0.0711 |
Standardized solution:
Loadings:
smodel2 %>%
filter(op == "=~") %>%
kable(
col.names = c(
"Factor",
"",
"Item",
"Loading",
"SE",
"z",
"p",
"CI lower bound",
"CI upper bound"
),
digits = 3
)| Factor | Item | Loading | SE | z | p | CI lower bound | CI upper bound | |
|---|---|---|---|---|---|---|---|---|
| PR | =~ | pr01 | 0.787 | 0.020 | 39.421 | 0.00 | 0.748 | 0.827 |
| PR | =~ | pr02 | 0.587 | 0.032 | 18.392 | 0.00 | 0.524 | 0.649 |
| PR | =~ | pr03 | 0.298 | 0.043 | 6.889 | 0.00 | 0.213 | 0.383 |
| PR | =~ | pr04 | 0.119 | 0.047 | 2.561 | 0.01 | 0.028 | 0.210 |
| PR | =~ | pr05 | 0.572 | 0.033 | 17.497 | 0.00 | 0.508 | 0.636 |
| PR | =~ | pr06 | 0.578 | 0.032 | 17.869 | 0.00 | 0.515 | 0.641 |
| PR | =~ | pr07 | 0.732 | 0.024 | 31.152 | 0.00 | 0.686 | 0.778 |
| PR | =~ | pr08 | 0.723 | 0.024 | 30.023 | 0.00 | 0.676 | 0.770 |
| PR | =~ | pr09 | 0.585 | 0.032 | 18.294 | 0.00 | 0.522 | 0.648 |
| PR | =~ | pr10 | 0.495 | 0.036 | 13.654 | 0.00 | 0.424 | 0.566 |
| CO | =~ | co01 | 0.738 | 0.024 | 30.465 | 0.00 | 0.691 | 0.786 |
| CO | =~ | co02 | 0.683 | 0.028 | 24.809 | 0.00 | 0.629 | 0.737 |
| CO | =~ | co03 | 0.507 | 0.037 | 13.884 | 0.00 | 0.436 | 0.579 |
| CO | =~ | co04 | 0.346 | 0.043 | 8.129 | 0.00 | 0.263 | 0.430 |
| CO | =~ | co05 | 0.779 | 0.022 | 35.829 | 0.00 | 0.736 | 0.821 |
| CO | =~ | co06 | 0.618 | 0.031 | 19.860 | 0.00 | 0.557 | 0.679 |
| CO | =~ | co08 | 0.333 | 0.043 | 7.737 | 0.00 | 0.248 | 0.417 |
| CO | =~ | co09 | 0.727 | 0.025 | 29.164 | 0.00 | 0.678 | 0.776 |
| CO | =~ | co10 | 0.605 | 0.032 | 18.998 | 0.00 | 0.542 | 0.667 |
| UT | =~ | ut01 | 0.774 | 0.021 | 37.585 | 0.00 | 0.733 | 0.814 |
| UT | =~ | ut02 | 0.834 | 0.017 | 50.561 | 0.00 | 0.802 | 0.867 |
| UT | =~ | ut03 | 0.495 | 0.036 | 13.693 | 0.00 | 0.424 | 0.565 |
| UT | =~ | ut04 | 0.466 | 0.037 | 12.481 | 0.00 | 0.393 | 0.539 |
| UT | =~ | ut05 | 0.654 | 0.028 | 23.305 | 0.00 | 0.599 | 0.708 |
| UT | =~ | ut06 | 0.748 | 0.022 | 33.540 | 0.00 | 0.704 | 0.791 |
| UT | =~ | ut07 | 0.569 | 0.033 | 17.431 | 0.00 | 0.505 | 0.633 |
| UT | =~ | ut08 | 0.624 | 0.030 | 21.028 | 0.00 | 0.566 | 0.682 |
| UT | =~ | ut09 | 0.658 | 0.028 | 23.643 | 0.00 | 0.603 | 0.712 |
| UT | =~ | ut11 | 0.521 | 0.035 | 14.903 | 0.00 | 0.452 | 0.589 |
| UT | =~ | ut12 | 0.689 | 0.026 | 26.593 | 0.00 | 0.639 | 0.740 |
| FA | =~ | fa01 | 0.831 | 0.019 | 43.634 | 0.00 | 0.793 | 0.868 |
| FA | =~ | fa02 | 0.404 | 0.041 | 9.937 | 0.00 | 0.325 | 0.484 |
| FA | =~ | fa03 | 0.164 | 0.047 | 3.519 | 0.00 | 0.073 | 0.256 |
| FA | =~ | fa04 | 0.343 | 0.043 | 8.031 | 0.00 | 0.259 | 0.427 |
| FA | =~ | fa05 | 0.844 | 0.018 | 46.107 | 0.00 | 0.808 | 0.880 |
| FA | =~ | fa06 | 0.597 | 0.032 | 18.480 | 0.00 | 0.533 | 0.660 |
| FA | =~ | fa07 | 0.271 | 0.045 | 6.067 | 0.00 | 0.183 | 0.359 |
| FA | =~ | fa08 | 0.382 | 0.041 | 9.209 | 0.00 | 0.301 | 0.463 |
| FA | =~ | fa09 | 0.477 | 0.038 | 12.585 | 0.00 | 0.403 | 0.551 |
| FA | =~ | fa10 | 0.595 | 0.032 | 18.373 | 0.00 | 0.531 | 0.658 |
| DE | =~ | de01 | 0.560 | 0.033 | 16.714 | 0.00 | 0.494 | 0.625 |
| DE | =~ | de02 | 0.699 | 0.026 | 26.998 | 0.00 | 0.648 | 0.749 |
| DE | =~ | de03 | 0.604 | 0.031 | 19.352 | 0.00 | 0.543 | 0.665 |
| DE | =~ | de05 | 0.531 | 0.035 | 15.227 | 0.00 | 0.463 | 0.599 |
| DE | =~ | de06 | 0.633 | 0.030 | 21.338 | 0.00 | 0.575 | 0.691 |
| DE | =~ | de07 | 0.607 | 0.031 | 19.535 | 0.00 | 0.546 | 0.668 |
| DE | =~ | de08 | 0.690 | 0.026 | 26.154 | 0.00 | 0.638 | 0.742 |
| DE | =~ | de09 | 0.319 | 0.043 | 7.441 | 0.00 | 0.235 | 0.403 |
| DE | =~ | de10 | 0.712 | 0.025 | 28.410 | 0.00 | 0.663 | 0.761 |
| DE | =~ | de11 | 0.242 | 0.045 | 5.408 | 0.00 | 0.154 | 0.330 |
| UN | =~ | un01 | 0.742 | 0.022 | 33.751 | 0.00 | 0.699 | 0.785 |
| UN | =~ | un02 | 0.831 | 0.016 | 52.514 | 0.00 | 0.800 | 0.862 |
| UN | =~ | un03 | 0.541 | 0.033 | 16.192 | 0.00 | 0.476 | 0.606 |
| UN | =~ | un04 | 0.735 | 0.022 | 32.778 | 0.00 | 0.691 | 0.779 |
| UN | =~ | un05 | 0.807 | 0.018 | 46.027 | 0.00 | 0.773 | 0.842 |
| UN | =~ | un06 | 0.494 | 0.036 | 13.869 | 0.00 | 0.424 | 0.563 |
| UN | =~ | un07 | 0.686 | 0.025 | 26.938 | 0.00 | 0.636 | 0.736 |
| UN | =~ | un08 | 0.753 | 0.021 | 35.450 | 0.00 | 0.711 | 0.795 |
| UN | =~ | un09 | 0.689 | 0.025 | 27.222 | 0.00 | 0.639 | 0.739 |
| UN | =~ | un10 | 0.709 | 0.024 | 29.476 | 0.00 | 0.662 | 0.756 |
| UN | =~ | un11 | 0.771 | 0.020 | 38.554 | 0.00 | 0.732 | 0.811 |
| UN | =~ | un12 | 0.736 | 0.022 | 32.957 | 0.00 | 0.692 | 0.780 |
| DT | =~ | PR | 0.957 | 0.015 | 65.735 | 0.00 | 0.928 | 0.985 |
| DT | =~ | CO | 0.697 | 0.030 | 23.591 | 0.00 | 0.639 | 0.755 |
| DT | =~ | UT | 0.748 | 0.025 | 29.635 | 0.00 | 0.699 | 0.798 |
| DT | =~ | FA | 0.635 | 0.033 | 19.234 | 0.00 | 0.571 | 0.700 |
| DT | =~ | DE | 0.941 | 0.016 | 60.642 | 0.00 | 0.911 | 0.972 |
| DT | =~ | UN | 0.389 | 0.042 | 9.209 | 0.00 | 0.306 | 0.472 |
Covariances:
smodel2 %>%
filter(op == "~~" & lhs != rhs) %>%
kable(
col.names = c(
"Factor",
"",
"Factor",
"Covariance",
"SE",
"z",
"p",
"CI lower bound",
"CI upper bound"
),
digits = 3
)| Factor | Factor | Covariance | SE | z | p | CI lower bound | CI upper bound |
|---|
Residuals:
smodel2 %>%
filter(op == "~~" & lhs == rhs) %>%
select(-(2:3)) %>%
kable(
col.names = c(
"Item",
"Residual",
"SE",
"z",
"p",
"CI lower bound",
"CI upper bound"
),
digits = 3
)| Item | Residual | SE | z | p | CI lower bound | CI upper bound |
|---|---|---|---|---|---|---|
| pr01 | 0.380 | 0.031 | 12.074 | 0.000 | 0.318 | 0.442 |
| pr02 | 0.656 | 0.037 | 17.528 | 0.000 | 0.583 | 0.729 |
| pr03 | 0.911 | 0.026 | 35.398 | 0.000 | 0.861 | 0.962 |
| pr04 | 0.986 | 0.011 | 88.834 | 0.000 | 0.964 | 1.008 |
| pr05 | 0.673 | 0.037 | 18.033 | 0.000 | 0.600 | 0.747 |
| pr06 | 0.666 | 0.037 | 17.817 | 0.000 | 0.593 | 0.739 |
| pr07 | 0.464 | 0.034 | 13.462 | 0.000 | 0.396 | 0.531 |
| pr08 | 0.477 | 0.035 | 13.693 | 0.000 | 0.409 | 0.545 |
| pr09 | 0.658 | 0.037 | 17.581 | 0.000 | 0.584 | 0.731 |
| pr10 | 0.755 | 0.036 | 21.013 | 0.000 | 0.684 | 0.825 |
| co01 | 0.455 | 0.036 | 12.729 | 0.000 | 0.385 | 0.525 |
| co02 | 0.534 | 0.038 | 14.210 | 0.000 | 0.460 | 0.608 |
| co03 | 0.743 | 0.037 | 20.019 | 0.000 | 0.670 | 0.815 |
| co04 | 0.880 | 0.030 | 29.818 | 0.000 | 0.822 | 0.938 |
| co05 | 0.393 | 0.034 | 11.621 | 0.000 | 0.327 | 0.460 |
| co06 | 0.618 | 0.038 | 16.064 | 0.000 | 0.543 | 0.693 |
| co08 | 0.889 | 0.029 | 31.060 | 0.000 | 0.833 | 0.945 |
| co09 | 0.472 | 0.036 | 13.032 | 0.000 | 0.401 | 0.543 |
| co10 | 0.634 | 0.038 | 16.479 | 0.000 | 0.559 | 0.710 |
| ut01 | 0.401 | 0.032 | 12.604 | 0.000 | 0.339 | 0.464 |
| ut02 | 0.304 | 0.028 | 11.029 | 0.000 | 0.250 | 0.358 |
| ut03 | 0.755 | 0.036 | 21.142 | 0.000 | 0.685 | 0.825 |
| ut04 | 0.783 | 0.035 | 22.492 | 0.000 | 0.715 | 0.851 |
| ut05 | 0.573 | 0.037 | 15.632 | 0.000 | 0.501 | 0.645 |
| ut06 | 0.441 | 0.033 | 13.238 | 0.000 | 0.376 | 0.506 |
| ut07 | 0.677 | 0.037 | 18.236 | 0.000 | 0.604 | 0.749 |
| ut08 | 0.610 | 0.037 | 16.463 | 0.000 | 0.538 | 0.683 |
| ut09 | 0.568 | 0.037 | 15.522 | 0.000 | 0.496 | 0.639 |
| ut11 | 0.729 | 0.036 | 20.030 | 0.000 | 0.658 | 0.800 |
| ut12 | 0.525 | 0.036 | 14.678 | 0.000 | 0.455 | 0.595 |
| fa01 | 0.310 | 0.032 | 9.805 | 0.000 | 0.248 | 0.372 |
| fa02 | 0.836 | 0.033 | 25.404 | 0.000 | 0.772 | 0.901 |
| fa03 | 0.973 | 0.015 | 63.285 | 0.000 | 0.943 | 1.003 |
| fa04 | 0.882 | 0.029 | 30.092 | 0.000 | 0.825 | 0.940 |
| fa05 | 0.287 | 0.031 | 9.297 | 0.000 | 0.227 | 0.348 |
| fa06 | 0.644 | 0.039 | 16.709 | 0.000 | 0.568 | 0.719 |
| fa07 | 0.927 | 0.024 | 38.250 | 0.000 | 0.879 | 0.974 |
| fa08 | 0.854 | 0.032 | 26.951 | 0.000 | 0.792 | 0.916 |
| fa09 | 0.773 | 0.036 | 21.374 | 0.000 | 0.702 | 0.843 |
| fa10 | 0.646 | 0.039 | 16.767 | 0.000 | 0.571 | 0.722 |
| de01 | 0.687 | 0.037 | 18.317 | 0.000 | 0.613 | 0.760 |
| de02 | 0.512 | 0.036 | 14.164 | 0.000 | 0.441 | 0.583 |
| de03 | 0.635 | 0.038 | 16.835 | 0.000 | 0.561 | 0.709 |
| de05 | 0.718 | 0.037 | 19.391 | 0.000 | 0.645 | 0.791 |
| de06 | 0.599 | 0.038 | 15.964 | 0.000 | 0.526 | 0.673 |
| de07 | 0.632 | 0.038 | 16.747 | 0.000 | 0.558 | 0.706 |
| de08 | 0.524 | 0.036 | 14.387 | 0.000 | 0.452 | 0.595 |
| de09 | 0.898 | 0.027 | 32.863 | 0.000 | 0.845 | 0.952 |
| de10 | 0.493 | 0.036 | 13.816 | 0.000 | 0.423 | 0.563 |
| de11 | 0.941 | 0.022 | 43.416 | 0.000 | 0.899 | 0.984 |
| un01 | 0.450 | 0.033 | 13.802 | 0.000 | 0.386 | 0.514 |
| un02 | 0.309 | 0.026 | 11.737 | 0.000 | 0.257 | 0.361 |
| un03 | 0.707 | 0.036 | 19.565 | 0.000 | 0.636 | 0.778 |
| un04 | 0.460 | 0.033 | 13.960 | 0.000 | 0.395 | 0.525 |
| un05 | 0.349 | 0.028 | 12.311 | 0.000 | 0.293 | 0.404 |
| un06 | 0.756 | 0.035 | 21.515 | 0.000 | 0.687 | 0.825 |
| un07 | 0.529 | 0.035 | 15.131 | 0.000 | 0.461 | 0.598 |
| un08 | 0.433 | 0.032 | 13.545 | 0.000 | 0.370 | 0.496 |
| un09 | 0.525 | 0.035 | 15.062 | 0.000 | 0.457 | 0.594 |
| un10 | 0.497 | 0.034 | 14.567 | 0.000 | 0.430 | 0.564 |
| un11 | 0.405 | 0.031 | 13.127 | 0.000 | 0.345 | 0.466 |
| un12 | 0.458 | 0.033 | 13.930 | 0.000 | 0.394 | 0.523 |
| PR | 0.084 | 0.028 | 3.025 | 0.002 | 0.030 | 0.139 |
| CO | 0.514 | 0.041 | 12.465 | 0.000 | 0.433 | 0.595 |
| UT | 0.440 | 0.038 | 11.638 | 0.000 | 0.366 | 0.514 |
| FA | 0.596 | 0.042 | 14.207 | 0.000 | 0.514 | 0.679 |
| DE | 0.114 | 0.029 | 3.897 | 0.000 | 0.057 | 0.171 |
| UN | 0.849 | 0.033 | 25.829 | 0.000 | 0.784 | 0.913 |
| DT | 1.000 | 0.000 | NA | NA | 1.000 | 1.000 |
semPaths(model2,
what = "std",
whatLabels = "est",
style = "lisrel",
residScale = 10,
theme = "colorblind",
rotation = 1,
layout = "tree",
cardinal = "lat cov",
curvePivot = TRUE,
sizeMan = 3,
sizeLat = 7)Excluded items: pr03, pr04, fa03, fa07, de11
Reason: low factor loadings
Stems:
pr03 Я считаю, что интеллектуальные системы ненадежны (R)pr04 Я считаю, что результаты работы интеллектуальных систем невозможно предсказать (R)fa03 Если интеллектуальная система перестает реагировать на запросы, мне с этим комфортноfa07 Я предпочту сам (сама) контролировать весь процесс нежели дам контроль интеллектуальной системе (R)de11 Я могу доверить искусственному интеллекту только рутинные задачи (например, уборка) (R)Model:
mdl3 <- "
PR =~ pr01 + pr02 + pr05 + pr06 + pr07 + pr08 + pr09 + pr10
CO =~ co01 + co02 + co03 + co04 + co05 + co06 + co08 + co09 + co10
UT =~ ut01 + ut02 + ut03 + ut04 + ut05 + ut06 + ut07 + ut08 + ut09 + ut11 + ut12
FA =~ fa01 + fa02 + fa04 + fa05 + fa06 + fa08 + fa09 + fa10
DE =~ de01 + de02 + de03 + de05 + de06 + de07 + de08 + de09 + de10
UN =~ un01 + un02 + un03 + un04 + un05 + un06 + un07 + un08 + un09 + un10 + un11 + un12
DT =~ PR + CO + UT + FA + DE + UN
"## lavaan 0.6-8 ended normally after 45 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 120
##
## Number of observations 495
##
## Model Test User Model:
##
## Test statistic 5261.509
## Degrees of freedom 1533
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## PR =~
## pr01 1.000
## pr02 0.738 0.056 13.173 0.000
## pr05 0.890 0.069 12.819 0.000
## pr06 0.805 0.062 12.996 0.000
## pr07 1.053 0.062 16.945 0.000
## pr08 0.852 0.051 16.773 0.000
## pr09 0.727 0.055 13.194 0.000
## pr10 0.669 0.061 11.001 0.000
## CO =~
## co01 1.000
## co02 0.894 0.062 14.492 0.000
## co03 0.653 0.061 10.711 0.000
## co04 0.474 0.065 7.277 0.000
## co05 1.088 0.066 16.546 0.000
## co06 0.862 0.066 13.089 0.000
## co08 0.435 0.062 6.978 0.000
## co09 0.977 0.063 15.433 0.000
## co10 0.835 0.065 12.814 0.000
## UT =~
## ut01 1.000
## ut02 1.080 0.055 19.757 0.000
## ut03 0.671 0.062 10.868 0.000
## ut04 0.635 0.062 10.231 0.000
## ut05 0.972 0.065 14.846 0.000
## ut06 1.007 0.058 17.348 0.000
## ut07 0.792 0.062 12.718 0.000
## ut08 0.807 0.057 14.098 0.000
## ut09 0.945 0.063 14.939 0.000
## ut11 0.789 0.068 11.523 0.000
## ut12 0.973 0.062 15.779 0.000
## FA =~
## fa01 1.000
## fa02 0.516 0.059 8.728 0.000
## fa04 0.397 0.055 7.215 0.000
## fa05 1.010 0.049 20.518 0.000
## fa06 0.691 0.052 13.376 0.000
## fa08 0.468 0.057 8.178 0.000
## fa09 0.604 0.058 10.363 0.000
## fa10 0.785 0.057 13.732 0.000
## DE =~
## de01 1.000
## de02 1.310 0.112 11.675 0.000
## de03 1.170 0.110 10.597 0.000
## de05 0.998 0.104 9.640 0.000
## de06 1.255 0.115 10.911 0.000
## de07 0.988 0.093 10.664 0.000
## de08 1.187 0.102 11.604 0.000
## de09 0.603 0.097 6.189 0.000
## de10 1.363 0.116 11.794 0.000
## UN =~
## un01 1.000
## un02 1.212 0.064 18.933 0.000
## un03 0.811 0.068 11.917 0.000
## un04 1.022 0.062 16.529 0.000
## un05 1.140 0.062 18.327 0.000
## un06 0.780 0.072 10.824 0.000
## un07 1.035 0.067 15.351 0.000
## un08 1.118 0.066 16.974 0.000
## un09 1.085 0.070 15.413 0.000
## un10 1.046 0.066 15.904 0.000
## un11 1.188 0.068 17.430 0.000
## un12 1.061 0.064 16.562 0.000
## DT =~
## PR 1.000
## CO 0.756 0.061 12.410 0.000
## UT 0.817 0.060 13.611 0.000
## FA 0.774 0.065 11.940 0.000
## DE 0.781 0.067 11.715 0.000
## UN 0.415 0.054 7.755 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .pr01 0.377 0.029 13.111 0.000
## .pr02 0.616 0.041 14.878 0.000
## .pr05 0.964 0.065 14.939 0.000
## .pr06 0.761 0.051 14.909 0.000
## .pr07 0.577 0.042 13.851 0.000
## .pr08 0.392 0.028 13.920 0.000
## .pr09 0.596 0.040 14.874 0.000
## .pr10 0.807 0.053 15.197 0.000
## .co01 0.528 0.040 13.134 0.000
## .co02 0.578 0.042 13.840 0.000
## .co03 0.774 0.052 14.983 0.000
## .co04 1.037 0.067 15.438 0.000
## .co05 0.485 0.039 12.379 0.000
## .co06 0.759 0.053 14.396 0.000
## .co08 0.959 0.062 15.464 0.000
## .co09 0.540 0.041 13.307 0.000
## .co10 0.760 0.052 14.482 0.000
## .ut01 0.442 0.033 13.480 0.000
## .ut02 0.336 0.027 12.276 0.000
## .ut03 0.927 0.061 15.252 0.000
## .ut04 0.959 0.063 15.318 0.000
## .ut05 0.835 0.057 14.611 0.000
## .ut06 0.528 0.038 13.820 0.000
## .ut07 0.863 0.058 15.013 0.000
## .ut08 0.672 0.046 14.773 0.000
## .ut09 0.775 0.053 14.590 0.000
## .ut11 1.106 0.073 15.177 0.000
## .ut12 0.690 0.048 14.370 0.000
## .fa01 0.364 0.035 10.321 0.000
## .fa02 1.164 0.076 15.308 0.000
## .fa04 1.041 0.067 15.452 0.000
## .fa05 0.347 0.035 9.969 0.000
## .fa06 0.759 0.052 14.556 0.000
## .fa08 1.102 0.072 15.365 0.000
## .fa09 1.081 0.072 15.106 0.000
## .fa10 0.914 0.063 14.471 0.000
## .de01 0.820 0.055 14.910 0.000
## .de02 0.680 0.049 14.009 0.000
## .de03 0.905 0.062 14.710 0.000
## .de05 0.971 0.065 15.041 0.000
## .de06 0.908 0.062 14.556 0.000
## .de07 0.627 0.043 14.679 0.000
## .de08 0.579 0.041 14.074 0.000
## .de09 1.296 0.083 15.544 0.000
## .de10 0.690 0.050 13.890 0.000
## .un01 0.497 0.035 14.368 0.000
## .un02 0.399 0.030 13.230 0.000
## .un03 0.966 0.063 15.272 0.000
## .un04 0.541 0.038 14.423 0.000
## .un05 0.423 0.031 13.641 0.000
## .un06 1.148 0.075 15.374 0.000
## .un07 0.731 0.050 14.739 0.000
## .un08 0.581 0.041 14.271 0.000
## .un09 0.792 0.054 14.725 0.000
## .un10 0.657 0.045 14.604 0.000
## .un11 0.584 0.041 14.092 0.000
## .un12 0.578 0.040 14.412 0.000
## .PR 0.045 0.017 2.670 0.008
## .CO 0.317 0.039 8.191 0.000
## .UT 0.294 0.034 8.763 0.000
## .FA 0.516 0.052 9.864 0.000
## .DE 0.044 0.013 3.465 0.001
## .UN 0.513 0.055 9.332 0.000
## DT 0.549 0.058 9.485 0.000
tibble(
`Model 3` = c(
"Chi-Squared",
"DF",
"p",
"GFI",
"AGFI",
"CFI",
"TLI",
"SRMR",
"RMSEA"
),
Value = round(fitmeasures(
model3,
c(
"chisq",
"df",
"pvalue",
"gfi",
"agfi",
"cfi",
"tli",
"srmr",
"rmsea"
)
), 4)
) %>% kable()| Model 3 | Value |
|---|---|
| Chi-Squared | 5261.5090 |
| DF | 1533.0000 |
| p | 0.0000 |
| GFI | 0.6731 |
| AGFI | 0.6476 |
| CFI | 0.7446 |
| TLI | 0.7341 |
| SRMR | 0.1013 |
| RMSEA | 0.0701 |
Standardized solution:
Loadings:
smodel3 %>%
filter(op == "=~") %>%
kable(
col.names = c(
"Factor",
"",
"Item",
"Loading",
"SE",
"z",
"p",
"CI lower bound",
"CI upper bound"
),
digits = 3
)| Factor | Item | Loading | SE | z | p | CI lower bound | CI upper bound | |
|---|---|---|---|---|---|---|---|---|
| PR | =~ | pr01 | 0.782 | 0.020 | 38.417 | 0 | 0.742 | 0.822 |
| PR | =~ | pr02 | 0.587 | 0.032 | 18.400 | 0 | 0.524 | 0.649 |
| PR | =~ | pr05 | 0.573 | 0.033 | 17.559 | 0 | 0.509 | 0.637 |
| PR | =~ | pr06 | 0.580 | 0.032 | 17.974 | 0 | 0.517 | 0.643 |
| PR | =~ | pr07 | 0.730 | 0.024 | 30.840 | 0 | 0.684 | 0.776 |
| PR | =~ | pr08 | 0.724 | 0.024 | 30.081 | 0 | 0.677 | 0.771 |
| PR | =~ | pr09 | 0.588 | 0.032 | 18.451 | 0 | 0.525 | 0.650 |
| PR | =~ | pr10 | 0.498 | 0.036 | 13.787 | 0 | 0.427 | 0.569 |
| CO | =~ | co01 | 0.738 | 0.024 | 30.452 | 0 | 0.690 | 0.785 |
| CO | =~ | co02 | 0.683 | 0.028 | 24.821 | 0 | 0.629 | 0.737 |
| CO | =~ | co03 | 0.508 | 0.037 | 13.911 | 0 | 0.436 | 0.580 |
| CO | =~ | co04 | 0.347 | 0.043 | 8.149 | 0 | 0.264 | 0.430 |
| CO | =~ | co05 | 0.779 | 0.022 | 35.840 | 0 | 0.736 | 0.821 |
| CO | =~ | co06 | 0.618 | 0.031 | 19.865 | 0 | 0.557 | 0.679 |
| CO | =~ | co08 | 0.333 | 0.043 | 7.740 | 0 | 0.249 | 0.417 |
| CO | =~ | co09 | 0.726 | 0.025 | 29.132 | 0 | 0.677 | 0.775 |
| CO | =~ | co10 | 0.605 | 0.032 | 19.048 | 0 | 0.543 | 0.668 |
| UT | =~ | ut01 | 0.774 | 0.021 | 37.612 | 0 | 0.734 | 0.814 |
| UT | =~ | ut02 | 0.834 | 0.017 | 50.496 | 0 | 0.802 | 0.867 |
| UT | =~ | ut03 | 0.493 | 0.036 | 13.613 | 0 | 0.422 | 0.564 |
| UT | =~ | ut04 | 0.466 | 0.037 | 12.469 | 0 | 0.393 | 0.539 |
| UT | =~ | ut05 | 0.654 | 0.028 | 23.325 | 0 | 0.599 | 0.709 |
| UT | =~ | ut06 | 0.748 | 0.022 | 33.579 | 0 | 0.704 | 0.792 |
| UT | =~ | ut07 | 0.569 | 0.033 | 17.471 | 0 | 0.506 | 0.633 |
| UT | =~ | ut08 | 0.625 | 0.030 | 21.049 | 0 | 0.566 | 0.683 |
| UT | =~ | ut09 | 0.657 | 0.028 | 23.629 | 0 | 0.603 | 0.712 |
| UT | =~ | ut11 | 0.520 | 0.035 | 14.879 | 0 | 0.452 | 0.589 |
| UT | =~ | ut12 | 0.690 | 0.026 | 26.601 | 0 | 0.639 | 0.740 |
| FA | =~ | fa01 | 0.836 | 0.019 | 44.351 | 0 | 0.799 | 0.873 |
| FA | =~ | fa02 | 0.403 | 0.041 | 9.877 | 0 | 0.323 | 0.483 |
| FA | =~ | fa04 | 0.337 | 0.043 | 7.848 | 0 | 0.253 | 0.421 |
| FA | =~ | fa05 | 0.845 | 0.018 | 45.872 | 0 | 0.809 | 0.881 |
| FA | =~ | fa06 | 0.589 | 0.033 | 18.019 | 0 | 0.525 | 0.653 |
| FA | =~ | fa08 | 0.379 | 0.042 | 9.114 | 0 | 0.298 | 0.461 |
| FA | =~ | fa09 | 0.471 | 0.038 | 12.352 | 0 | 0.396 | 0.546 |
| FA | =~ | fa10 | 0.602 | 0.032 | 18.816 | 0 | 0.540 | 0.665 |
| DE | =~ | de01 | 0.562 | 0.033 | 16.829 | 0 | 0.497 | 0.627 |
| DE | =~ | de02 | 0.699 | 0.026 | 27.038 | 0 | 0.649 | 0.750 |
| DE | =~ | de03 | 0.603 | 0.031 | 19.291 | 0 | 0.542 | 0.665 |
| DE | =~ | de05 | 0.529 | 0.035 | 15.120 | 0 | 0.460 | 0.598 |
| DE | =~ | de06 | 0.630 | 0.030 | 21.094 | 0 | 0.571 | 0.688 |
| DE | =~ | de07 | 0.609 | 0.031 | 19.655 | 0 | 0.548 | 0.670 |
| DE | =~ | de08 | 0.692 | 0.026 | 26.365 | 0 | 0.641 | 0.744 |
| DE | =~ | de09 | 0.310 | 0.043 | 7.189 | 0 | 0.226 | 0.395 |
| DE | =~ | de10 | 0.711 | 0.025 | 28.245 | 0 | 0.661 | 0.760 |
| UN | =~ | un01 | 0.742 | 0.022 | 33.751 | 0 | 0.699 | 0.785 |
| UN | =~ | un02 | 0.831 | 0.016 | 52.499 | 0 | 0.800 | 0.862 |
| UN | =~ | un03 | 0.541 | 0.033 | 16.192 | 0 | 0.476 | 0.606 |
| UN | =~ | un04 | 0.735 | 0.022 | 32.773 | 0 | 0.691 | 0.779 |
| UN | =~ | un05 | 0.807 | 0.018 | 46.033 | 0 | 0.773 | 0.842 |
| UN | =~ | un06 | 0.493 | 0.036 | 13.855 | 0 | 0.424 | 0.563 |
| UN | =~ | un07 | 0.686 | 0.025 | 26.955 | 0 | 0.637 | 0.736 |
| UN | =~ | un08 | 0.753 | 0.021 | 35.453 | 0 | 0.711 | 0.795 |
| UN | =~ | un09 | 0.689 | 0.025 | 27.225 | 0 | 0.639 | 0.739 |
| UN | =~ | un10 | 0.709 | 0.024 | 29.486 | 0 | 0.662 | 0.756 |
| UN | =~ | un11 | 0.771 | 0.020 | 38.556 | 0 | 0.732 | 0.810 |
| UN | =~ | un12 | 0.736 | 0.022 | 32.962 | 0 | 0.692 | 0.780 |
| DT | =~ | PR | 0.961 | 0.014 | 66.441 | 0 | 0.933 | 0.990 |
| DT | =~ | CO | 0.705 | 0.029 | 24.250 | 0 | 0.648 | 0.762 |
| DT | =~ | UT | 0.745 | 0.025 | 29.284 | 0 | 0.695 | 0.795 |
| DT | =~ | FA | 0.624 | 0.034 | 18.548 | 0 | 0.558 | 0.690 |
| DT | =~ | DE | 0.941 | 0.016 | 60.324 | 0 | 0.910 | 0.971 |
| DT | =~ | UN | 0.395 | 0.042 | 9.399 | 0 | 0.313 | 0.477 |
Covariances:
smodel3 %>%
filter(op == "~~" & lhs != rhs) %>%
kable(
col.names = c(
"Factor",
"",
"Factor",
"Covariance",
"SE",
"z",
"p",
"CI lower bound",
"CI upper bound"
),
digits = 3
)| Factor | Factor | Covariance | SE | z | p | CI lower bound | CI upper bound |
|---|
Residuals:
smodel3 %>%
filter(op == "~~" & lhs == rhs) %>%
select(-(2:3)) %>%
kable(
col.names = c(
"Item",
"Residual",
"SE",
"z",
"p",
"CI lower bound",
"CI upper bound"
),
digits = 3
)| Item | Residual | SE | z | p | CI lower bound | CI upper bound |
|---|---|---|---|---|---|---|
| pr01 | 0.388 | 0.032 | 12.200 | 0.000 | 0.326 | 0.451 |
| pr02 | 0.656 | 0.037 | 17.513 | 0.000 | 0.582 | 0.729 |
| pr05 | 0.672 | 0.037 | 17.986 | 0.000 | 0.599 | 0.745 |
| pr06 | 0.664 | 0.037 | 17.747 | 0.000 | 0.591 | 0.737 |
| pr07 | 0.467 | 0.035 | 13.509 | 0.000 | 0.399 | 0.535 |
| pr08 | 0.476 | 0.035 | 13.666 | 0.000 | 0.408 | 0.544 |
| pr09 | 0.655 | 0.037 | 17.485 | 0.000 | 0.581 | 0.728 |
| pr10 | 0.752 | 0.036 | 20.870 | 0.000 | 0.681 | 0.822 |
| co01 | 0.456 | 0.036 | 12.746 | 0.000 | 0.386 | 0.526 |
| co02 | 0.534 | 0.038 | 14.218 | 0.000 | 0.460 | 0.608 |
| co03 | 0.742 | 0.037 | 20.004 | 0.000 | 0.669 | 0.815 |
| co04 | 0.880 | 0.030 | 29.771 | 0.000 | 0.822 | 0.938 |
| co05 | 0.394 | 0.034 | 11.637 | 0.000 | 0.327 | 0.460 |
| co06 | 0.618 | 0.038 | 16.073 | 0.000 | 0.543 | 0.693 |
| co08 | 0.889 | 0.029 | 31.064 | 0.000 | 0.833 | 0.945 |
| co09 | 0.473 | 0.036 | 13.054 | 0.000 | 0.402 | 0.544 |
| co10 | 0.634 | 0.038 | 16.465 | 0.000 | 0.558 | 0.709 |
| ut01 | 0.401 | 0.032 | 12.597 | 0.000 | 0.339 | 0.464 |
| ut02 | 0.304 | 0.028 | 11.030 | 0.000 | 0.250 | 0.358 |
| ut03 | 0.757 | 0.036 | 21.220 | 0.000 | 0.687 | 0.827 |
| ut04 | 0.783 | 0.035 | 22.504 | 0.000 | 0.715 | 0.851 |
| ut05 | 0.573 | 0.037 | 15.622 | 0.000 | 0.501 | 0.644 |
| ut06 | 0.441 | 0.033 | 13.228 | 0.000 | 0.375 | 0.506 |
| ut07 | 0.676 | 0.037 | 18.209 | 0.000 | 0.603 | 0.749 |
| ut08 | 0.610 | 0.037 | 16.452 | 0.000 | 0.537 | 0.683 |
| ut09 | 0.568 | 0.037 | 15.523 | 0.000 | 0.496 | 0.640 |
| ut11 | 0.729 | 0.036 | 20.046 | 0.000 | 0.658 | 0.801 |
| ut12 | 0.525 | 0.036 | 14.674 | 0.000 | 0.454 | 0.595 |
| fa01 | 0.301 | 0.032 | 9.535 | 0.000 | 0.239 | 0.363 |
| fa02 | 0.838 | 0.033 | 25.501 | 0.000 | 0.773 | 0.902 |
| fa04 | 0.886 | 0.029 | 30.644 | 0.000 | 0.830 | 0.943 |
| fa05 | 0.287 | 0.031 | 9.210 | 0.000 | 0.226 | 0.347 |
| fa06 | 0.653 | 0.039 | 16.940 | 0.000 | 0.577 | 0.728 |
| fa08 | 0.856 | 0.032 | 27.148 | 0.000 | 0.794 | 0.918 |
| fa09 | 0.778 | 0.036 | 21.634 | 0.000 | 0.707 | 0.848 |
| fa10 | 0.637 | 0.039 | 16.511 | 0.000 | 0.561 | 0.713 |
| de01 | 0.684 | 0.038 | 18.225 | 0.000 | 0.611 | 0.758 |
| de02 | 0.511 | 0.036 | 14.134 | 0.000 | 0.440 | 0.582 |
| de03 | 0.636 | 0.038 | 16.847 | 0.000 | 0.562 | 0.710 |
| de05 | 0.720 | 0.037 | 19.459 | 0.000 | 0.648 | 0.793 |
| de06 | 0.603 | 0.038 | 16.045 | 0.000 | 0.530 | 0.677 |
| de07 | 0.629 | 0.038 | 16.673 | 0.000 | 0.555 | 0.703 |
| de08 | 0.521 | 0.036 | 14.311 | 0.000 | 0.449 | 0.592 |
| de09 | 0.904 | 0.027 | 33.806 | 0.000 | 0.851 | 0.956 |
| de10 | 0.495 | 0.036 | 13.835 | 0.000 | 0.425 | 0.565 |
| un01 | 0.450 | 0.033 | 13.803 | 0.000 | 0.386 | 0.514 |
| un02 | 0.309 | 0.026 | 11.739 | 0.000 | 0.257 | 0.361 |
| un03 | 0.707 | 0.036 | 19.566 | 0.000 | 0.636 | 0.778 |
| un04 | 0.460 | 0.033 | 13.962 | 0.000 | 0.395 | 0.525 |
| un05 | 0.348 | 0.028 | 12.311 | 0.000 | 0.293 | 0.404 |
| un06 | 0.757 | 0.035 | 21.530 | 0.000 | 0.688 | 0.825 |
| un07 | 0.529 | 0.035 | 15.127 | 0.000 | 0.460 | 0.597 |
| un08 | 0.433 | 0.032 | 13.546 | 0.000 | 0.370 | 0.496 |
| un09 | 0.525 | 0.035 | 15.062 | 0.000 | 0.457 | 0.594 |
| un10 | 0.497 | 0.034 | 14.566 | 0.000 | 0.430 | 0.564 |
| un11 | 0.405 | 0.031 | 13.128 | 0.000 | 0.345 | 0.466 |
| un12 | 0.458 | 0.033 | 13.930 | 0.000 | 0.394 | 0.523 |
| PR | 0.076 | 0.028 | 2.729 | 0.006 | 0.021 | 0.130 |
| CO | 0.503 | 0.041 | 12.262 | 0.000 | 0.422 | 0.583 |
| UT | 0.445 | 0.038 | 11.731 | 0.000 | 0.370 | 0.519 |
| FA | 0.611 | 0.042 | 14.542 | 0.000 | 0.528 | 0.693 |
| DE | 0.115 | 0.029 | 3.932 | 0.000 | 0.058 | 0.173 |
| UN | 0.844 | 0.033 | 25.430 | 0.000 | 0.779 | 0.909 |
| DT | 1.000 | 0.000 | NA | NA | 1.000 | 1.000 |
semPaths(model3,
what = "std",
whatLabels = "est",
style = "lisrel",
residScale = 10,
theme = "colorblind",
rotation = 1,
layout = "tree",
cardinal = "lat cov",
curvePivot = TRUE,
sizeMan = 3,
sizeLat = 7)lavPredict(model3, taia %>% select(all_of(taia_items_2))) %>%
as_tibble() %>%
mutate(id = taia$id) %>%
full_join(
taia %>%
select(id, all_of(taia_items_2)) %>%
pivot_longer(cols = all_of(taia_items_2)) %>%
mutate(name = toupper(str_replace_all(name, "[:digit:]{2}", ""))) %>%
group_by(id, name) %>%
summarise(score = sum(value)) %>%
pivot_wider(id_cols = id, names_from = name, names_prefix = "s_", values_from = score) %>%
relocate(after = c(id, s_PR, s_CO, s_UT, s_FA, s_DE, s_UN)) %>%
mutate(s_DT = s_PR + s_CO + s_UT + s_FA + s_DE + s_UN)
) -> predicted_and_direct_sums## `summarise()` regrouping output by 'id' (override with `.groups` argument)
## Joining, by = "id"
ggcorrplot(cor(predicted_and_direct_sums %>% select(-id))[-(1:7), -(8:14)],
lab = TRUE,
colors = c("indianred1", "white", "royalblue1"))Graph shows high correlation between fitted values and direct sums, so we can work with the later.
ggcorrplot(cor(predicted_and_direct_sums %>%
select(-id, -PR, -CO, -UT, -FA, -DE, -UN, -DT)),
type = "lower", lab = TRUE,
colors = c("indianred1", "white", "royalblue1"))Measures:
modif3 %>%
filter(op == "=~") %>%
arrange(lhs) %>%
kable(digits = 2,
col.names = c("Factor", "", "Item", "Modification Index", "epc", "sepc.lv", "sepc.all", "sepc.nox"))| Factor | Item | Modification Index | epc | sepc.lv | sepc.all | sepc.nox | |
|---|---|---|---|---|---|---|---|
| CO | =~ | fa06 | 96.43 | 0.62 | 0.49 | 0.46 | 0.46 |
| CO | =~ | fa04 | 61.19 | 0.56 | 0.45 | 0.41 | 0.41 |
| CO | =~ | fa02 | 46.88 | -0.52 | -0.41 | -0.35 | -0.35 |
| CO | =~ | fa08 | 44.78 | -0.50 | -0.39 | -0.35 | -0.35 |
| CO | =~ | ut03 | 34.67 | -0.43 | -0.34 | -0.31 | -0.31 |
| CO | =~ | fa09 | 33.63 | -0.43 | -0.34 | -0.29 | -0.29 |
| CO | =~ | ut11 | 26.27 | 0.41 | 0.32 | 0.26 | 0.26 |
| CO | =~ | un07 | 22.01 | 0.26 | 0.21 | 0.18 | 0.18 |
| CO | =~ | ut02 | 20.42 | -0.22 | -0.18 | -0.17 | -0.17 |
| CO | =~ | ut01 | 18.65 | -0.23 | -0.18 | -0.18 | -0.18 |
| CO | =~ | un10 | 9.29 | 0.16 | 0.13 | 0.11 | 0.11 |
| CO | =~ | de05 | 7.82 | -0.25 | -0.20 | -0.17 | -0.17 |
| CO | =~ | ut05 | 7.50 | 0.19 | 0.15 | 0.13 | 0.13 |
| CO | =~ | ut08 | 7.42 | 0.17 | 0.14 | 0.13 | 0.13 |
| CO | =~ | de09 | 5.24 | -0.23 | -0.19 | -0.16 | -0.16 |
| CO | =~ | fa10 | 4.90 | -0.15 | -0.12 | -0.10 | -0.10 |
| CO | =~ | pr09 | 4.84 | -0.16 | -0.13 | -0.13 | -0.13 |
| CO | =~ | fa05 | 4.82 | 0.11 | 0.09 | 0.08 | 0.08 |
| CO | =~ | de02 | 3.94 | 0.16 | 0.12 | 0.11 | 0.11 |
| CO | =~ | un06 | 3.94 | -0.14 | -0.11 | -0.09 | -0.09 |
| CO | =~ | ut04 | 3.52 | -0.14 | -0.11 | -0.10 | -0.10 |
| CO | =~ | un11 | 3.15 | -0.09 | -0.07 | -0.06 | -0.06 |
| CO | =~ | ut07 | 3.14 | 0.13 | 0.10 | 0.09 | 0.09 |
| CO | =~ | ut12 | 3.12 | 0.11 | 0.09 | 0.08 | 0.08 |
| CO | =~ | de03 | 2.76 | 0.15 | 0.12 | 0.10 | 0.10 |
| CO | =~ | pr05 | 2.41 | 0.14 | 0.11 | 0.09 | 0.09 |
| CO | =~ | de01 | 2.24 | 0.13 | 0.10 | 0.09 | 0.09 |
| CO | =~ | ut09 | 1.95 | 0.10 | 0.08 | 0.06 | 0.06 |
| CO | =~ | un12 | 1.92 | -0.07 | -0.06 | -0.05 | -0.05 |
| CO | =~ | pr02 | 1.77 | 0.10 | 0.08 | 0.08 | 0.08 |
| CO | =~ | pr07 | 1.73 | 0.10 | 0.08 | 0.07 | 0.07 |
| CO | =~ | un01 | 1.60 | -0.06 | -0.05 | -0.04 | -0.04 |
| CO | =~ | un09 | 1.60 | 0.07 | 0.06 | 0.05 | 0.05 |
| CO | =~ | de08 | 1.43 | -0.09 | -0.07 | -0.07 | -0.07 |
| CO | =~ | un08 | 1.19 | -0.06 | -0.04 | -0.04 | -0.04 |
| CO | =~ | un05 | 0.99 | -0.04 | -0.03 | -0.03 | -0.03 |
| CO | =~ | de10 | 0.79 | 0.07 | 0.06 | 0.05 | 0.05 |
| CO | =~ | pr06 | 0.54 | -0.06 | -0.05 | -0.04 | -0.04 |
| CO | =~ | fa01 | 0.39 | -0.03 | -0.03 | -0.02 | -0.02 |
| CO | =~ | un04 | 0.18 | 0.02 | 0.02 | 0.01 | 0.01 |
| CO | =~ | de07 | 0.11 | -0.02 | -0.02 | -0.02 | -0.02 |
| CO | =~ | un02 | 0.10 | -0.01 | -0.01 | -0.01 | -0.01 |
| CO | =~ | pr01 | 0.09 | 0.02 | 0.02 | 0.02 | 0.02 |
| CO | =~ | pr08 | 0.08 | -0.02 | -0.01 | -0.01 | -0.01 |
| CO | =~ | un03 | 0.01 | -0.01 | 0.00 | 0.00 | 0.00 |
| CO | =~ | ut06 | 0.01 | 0.01 | 0.00 | 0.00 | 0.00 |
| CO | =~ | de06 | 0.00 | -0.01 | 0.00 | 0.00 | 0.00 |
| CO | =~ | pr10 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| DE | =~ | fa06 | 131.37 | 1.07 | 0.66 | 0.61 | 0.61 |
| DE | =~ | ut11 | 77.11 | 1.13 | 0.70 | 0.56 | 0.56 |
| DE | =~ | co03 | 66.49 | 0.84 | 0.52 | 0.51 | 0.51 |
| DE | =~ | fa02 | 63.44 | -0.89 | -0.55 | -0.46 | -0.46 |
| DE | =~ | co08 | 44.14 | -0.75 | -0.46 | -0.44 | -0.44 |
| DE | =~ | co04 | 37.47 | 0.72 | 0.44 | 0.41 | 0.41 |
| DE | =~ | ut01 | 37.12 | -0.54 | -0.33 | -0.31 | -0.31 |
| DE | =~ | fa08 | 34.39 | -0.64 | -0.39 | -0.34 | -0.34 |
| DE | =~ | fa09 | 33.97 | -0.63 | -0.39 | -0.33 | -0.33 |
| DE | =~ | pr05 | 30.04 | 1.61 | 0.99 | 0.83 | 0.83 |
| DE | =~ | co09 | 22.90 | -0.45 | -0.28 | -0.26 | -0.26 |
| DE | =~ | fa04 | 22.02 | 0.49 | 0.30 | 0.28 | 0.28 |
| DE | =~ | ut02 | 18.30 | -0.35 | -0.22 | -0.20 | -0.20 |
| DE | =~ | un06 | 15.22 | -0.36 | -0.22 | -0.18 | -0.18 |
| DE | =~ | ut12 | 12.92 | 0.38 | 0.23 | 0.20 | 0.20 |
| DE | =~ | co01 | 11.78 | 0.32 | 0.20 | 0.18 | 0.18 |
| DE | =~ | co05 | 10.33 | -0.30 | -0.19 | -0.17 | -0.17 |
| DE | =~ | pr01 | 9.14 | -0.65 | -0.40 | -0.41 | -0.41 |
| DE | =~ | fa10 | 7.55 | -0.28 | -0.17 | -0.14 | -0.14 |
| DE | =~ | ut08 | 6.22 | 0.25 | 0.16 | 0.15 | 0.15 |
| DE | =~ | ut03 | 5.98 | -0.29 | -0.18 | -0.16 | -0.16 |
| DE | =~ | un07 | 5.76 | 0.18 | 0.11 | 0.09 | 0.09 |
| DE | =~ | pr06 | 4.51 | -0.55 | -0.34 | -0.32 | -0.32 |
| DE | =~ | fa05 | 4.41 | 0.17 | 0.10 | 0.09 | 0.09 |
| DE | =~ | un11 | 3.96 | -0.14 | -0.08 | -0.07 | -0.07 |
| DE | =~ | un09 | 3.57 | 0.15 | 0.09 | 0.07 | 0.07 |
| DE | =~ | pr08 | 3.44 | -0.38 | -0.23 | -0.26 | -0.26 |
| DE | =~ | un12 | 1.25 | 0.07 | 0.05 | 0.04 | 0.04 |
| DE | =~ | pr07 | 1.22 | 0.28 | 0.17 | 0.15 | 0.15 |
| DE | =~ | ut09 | 1.11 | 0.12 | 0.07 | 0.06 | 0.06 |
| DE | =~ | co02 | 1.01 | 0.09 | 0.06 | 0.06 | 0.06 |
| DE | =~ | un04 | 1.00 | -0.06 | -0.04 | -0.04 | -0.04 |
| DE | =~ | pr10 | 0.91 | -0.25 | -0.15 | -0.15 | -0.15 |
| DE | =~ | co06 | 0.90 | -0.10 | -0.06 | -0.06 | -0.06 |
| DE | =~ | ut07 | 0.75 | 0.10 | 0.06 | 0.05 | 0.05 |
| DE | =~ | un05 | 0.73 | 0.05 | 0.03 | 0.03 | 0.03 |
| DE | =~ | un02 | 0.66 | -0.05 | -0.03 | -0.03 | -0.03 |
| DE | =~ | ut06 | 0.60 | 0.07 | 0.04 | 0.04 | 0.04 |
| DE | =~ | un01 | 0.29 | 0.03 | 0.02 | 0.02 | 0.02 |
| DE | =~ | ut05 | 0.29 | 0.06 | 0.04 | 0.03 | 0.03 |
| DE | =~ | pr02 | 0.16 | 0.10 | 0.06 | 0.06 | 0.06 |
| DE | =~ | un03 | 0.12 | 0.03 | 0.02 | 0.02 | 0.02 |
| DE | =~ | fa01 | 0.11 | -0.03 | -0.02 | -0.01 | -0.01 |
| DE | =~ | ut04 | 0.06 | 0.03 | 0.02 | 0.02 | 0.02 |
| DE | =~ | pr09 | 0.01 | 0.02 | 0.01 | 0.02 | 0.02 |
| DE | =~ | co10 | 0.01 | 0.01 | 0.01 | 0.01 | 0.01 |
| DE | =~ | un08 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| DE | =~ | un10 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| DT | =~ | fa06 | 139.75 | 0.95 | 0.71 | 0.65 | 0.65 |
| DT | =~ | co03 | 79.85 | 0.82 | 0.61 | 0.59 | 0.59 |
| DT | =~ | fa02 | 75.65 | -0.84 | -0.62 | -0.53 | -0.53 |
| DT | =~ | ut11 | 71.86 | 0.98 | 0.73 | 0.59 | 0.59 |
| DT | =~ | co04 | 62.54 | 0.82 | 0.61 | 0.56 | 0.56 |
| DT | =~ | co08 | 60.53 | -0.78 | -0.57 | -0.55 | -0.55 |
| DT | =~ | fa08 | 44.54 | -0.62 | -0.46 | -0.41 | -0.41 |
| DT | =~ | fa09 | 38.52 | -0.58 | -0.43 | -0.37 | -0.37 |
| DT | =~ | ut01 | 37.14 | -0.48 | -0.36 | -0.34 | -0.34 |
| DT | =~ | co09 | 28.42 | -0.44 | -0.33 | -0.31 | -0.31 |
| DT | =~ | de05 | 23.82 | 1.77 | 1.31 | 1.13 | 1.13 |
| DT | =~ | pr05 | 23.22 | 2.61 | 1.93 | 1.61 | 1.61 |
| DT | =~ | ut02 | 16.84 | -0.30 | -0.22 | -0.21 | -0.21 |
| DT | =~ | un06 | 15.87 | -0.30 | -0.23 | -0.18 | -0.18 |
| DT | =~ | fa04 | 15.80 | 0.36 | 0.27 | 0.25 | 0.25 |
| DT | =~ | co05 | 13.51 | -0.31 | -0.23 | -0.20 | -0.20 |
| DT | =~ | ut12 | 12.72 | 0.34 | 0.25 | 0.22 | 0.22 |
| DT | =~ | co01 | 10.19 | 0.27 | 0.20 | 0.18 | 0.18 |
| DT | =~ | ut08 | 8.64 | 0.27 | 0.20 | 0.19 | 0.19 |
| DT | =~ | de10 | 8.29 | -0.99 | -0.73 | -0.62 | -0.62 |
| DT | =~ | de07 | 7.14 | 0.81 | 0.60 | 0.60 | 0.60 |
| DT | =~ | ut03 | 7.03 | -0.28 | -0.21 | -0.19 | -0.19 |
| DT | =~ | de09 | 6.76 | 1.04 | 0.77 | 0.64 | 0.64 |
| DT | =~ | fa10 | 6.35 | -0.22 | -0.17 | -0.14 | -0.14 |
| DT | =~ | un11 | 4.86 | -0.13 | -0.09 | -0.08 | -0.08 |
| DT | =~ | de03 | 4.67 | -0.78 | -0.58 | -0.49 | -0.49 |
| DT | =~ | un07 | 3.71 | 0.12 | 0.09 | 0.08 | 0.08 |
| DT | =~ | fa05 | 2.62 | 0.11 | 0.08 | 0.08 | 0.08 |
| DT | =~ | de08 | 1.98 | -0.44 | -0.32 | -0.31 | -0.31 |
| DT | =~ | pr01 | 1.97 | -0.58 | -0.43 | -0.44 | -0.44 |
| DT | =~ | co02 | 1.83 | 0.11 | 0.08 | 0.08 | 0.08 |
| DT | =~ | un01 | 1.77 | 0.07 | 0.05 | 0.05 | 0.05 |
| DT | =~ | co06 | 1.66 | -0.12 | -0.09 | -0.08 | -0.08 |
| DT | =~ | ut07 | 1.64 | 0.13 | 0.10 | 0.09 | 0.09 |
| DT | =~ | pr08 | 1.52 | -0.48 | -0.35 | -0.39 | -0.39 |
| DT | =~ | un12 | 1.50 | 0.07 | 0.05 | 0.05 | 0.05 |
| DT | =~ | ut09 | 1.41 | 0.12 | 0.09 | 0.07 | 0.07 |
| DT | =~ | un09 | 1.31 | 0.07 | 0.06 | 0.04 | 0.04 |
| DT | =~ | un02 | 1.26 | -0.05 | -0.04 | -0.04 | -0.04 |
| DT | =~ | de02 | 1.26 | -0.38 | -0.28 | -0.24 | -0.24 |
| DT | =~ | pr10 | 1.22 | -0.53 | -0.39 | -0.38 | -0.38 |
| DT | =~ | pr07 | 1.16 | 0.51 | 0.38 | 0.34 | 0.34 |
| DT | =~ | un05 | 1.10 | 0.05 | 0.04 | 0.03 | 0.03 |
| DT | =~ | pr09 | 0.89 | -0.40 | -0.30 | -0.31 | -0.31 |
| DT | =~ | ut06 | 0.84 | 0.08 | 0.06 | 0.05 | 0.05 |
| DT | =~ | pr06 | 0.47 | -0.33 | -0.25 | -0.23 | -0.23 |
| DT | =~ | un04 | 0.41 | -0.03 | -0.03 | -0.02 | -0.02 |
| DT | =~ | ut05 | 0.20 | 0.05 | 0.03 | 0.03 | 0.03 |
| DT | =~ | ut04 | 0.20 | -0.05 | -0.04 | -0.03 | -0.03 |
| DT | =~ | co10 | 0.17 | 0.04 | 0.03 | 0.03 | 0.03 |
| DT | =~ | de01 | 0.12 | 0.12 | 0.09 | 0.08 | 0.08 |
| DT | =~ | un03 | 0.09 | 0.02 | 0.02 | 0.01 | 0.01 |
| DT | =~ | de06 | 0.09 | 0.11 | 0.08 | 0.07 | 0.07 |
| DT | =~ | un10 | 0.07 | -0.02 | -0.01 | -0.01 | -0.01 |
| DT | =~ | un08 | 0.03 | 0.01 | 0.01 | 0.01 | 0.01 |
| DT | =~ | pr02 | 0.00 | 0.02 | 0.01 | 0.01 | 0.01 |
| DT | =~ | fa01 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| FA | =~ | co08 | 21.44 | -0.28 | -0.25 | -0.24 | -0.24 |
| FA | =~ | co03 | 19.45 | 0.24 | 0.22 | 0.22 | 0.22 |
| FA | =~ | co01 | 17.95 | 0.20 | 0.19 | 0.17 | 0.17 |
| FA | =~ | ut11 | 15.85 | 0.26 | 0.24 | 0.19 | 0.19 |
| FA | =~ | co04 | 15.75 | 0.25 | 0.23 | 0.21 | 0.21 |
| FA | =~ | un06 | 11.90 | -0.20 | -0.19 | -0.15 | -0.15 |
| FA | =~ | pr10 | 10.43 | -0.21 | -0.19 | -0.18 | -0.18 |
| FA | =~ | de10 | 9.04 | 0.18 | 0.17 | 0.14 | 0.14 |
| FA | =~ | co06 | 8.52 | -0.16 | -0.15 | -0.13 | -0.13 |
| FA | =~ | un03 | 8.16 | -0.15 | -0.14 | -0.12 | -0.12 |
| FA | =~ | ut01 | 7.87 | -0.12 | -0.11 | -0.11 | -0.11 |
| FA | =~ | co09 | 7.36 | -0.13 | -0.12 | -0.11 | -0.11 |
| FA | =~ | co02 | 6.78 | 0.13 | 0.12 | 0.11 | 0.11 |
| FA | =~ | de06 | 6.63 | 0.18 | 0.16 | 0.13 | 0.13 |
| FA | =~ | pr06 | 5.63 | -0.15 | -0.14 | -0.13 | -0.13 |
| FA | =~ | un01 | 4.88 | 0.09 | 0.08 | 0.08 | 0.08 |
| FA | =~ | un11 | 4.13 | -0.09 | -0.08 | -0.07 | -0.07 |
| FA | =~ | ut02 | 2.84 | -0.07 | -0.06 | -0.06 | -0.06 |
| FA | =~ | pr05 | 2.81 | 0.12 | 0.11 | 0.09 | 0.09 |
| FA | =~ | co10 | 2.63 | -0.09 | -0.08 | -0.07 | -0.07 |
| FA | =~ | un05 | 2.32 | 0.06 | 0.05 | 0.05 | 0.05 |
| FA | =~ | de05 | 2.25 | -0.10 | -0.10 | -0.08 | -0.08 |
| FA | =~ | de03 | 2.05 | 0.10 | 0.09 | 0.08 | 0.08 |
| FA | =~ | de07 | 1.72 | -0.07 | -0.07 | -0.07 | -0.07 |
| FA | =~ | pr08 | 1.67 | -0.06 | -0.06 | -0.06 | -0.06 |
| FA | =~ | pr02 | 1.53 | -0.07 | -0.06 | -0.07 | -0.07 |
| FA | =~ | co05 | 1.34 | -0.05 | -0.05 | -0.05 | -0.05 |
| FA | =~ | pr07 | 1.24 | 0.06 | 0.06 | 0.05 | 0.05 |
| FA | =~ | ut03 | 1.17 | 0.06 | 0.06 | 0.05 | 0.05 |
| FA | =~ | un04 | 1.15 | -0.04 | -0.04 | -0.04 | -0.04 |
| FA | =~ | un10 | 0.80 | -0.04 | -0.04 | -0.03 | -0.03 |
| FA | =~ | ut04 | 0.64 | 0.05 | 0.04 | 0.04 | 0.04 |
| FA | =~ | ut08 | 0.57 | 0.04 | 0.04 | 0.03 | 0.03 |
| FA | =~ | un07 | 0.56 | 0.04 | 0.03 | 0.03 | 0.03 |
| FA | =~ | ut07 | 0.54 | 0.04 | 0.04 | 0.03 | 0.03 |
| FA | =~ | un08 | 0.53 | 0.03 | 0.03 | 0.02 | 0.02 |
| FA | =~ | pr09 | 0.45 | -0.04 | -0.03 | -0.04 | -0.04 |
| FA | =~ | de02 | 0.42 | 0.04 | 0.04 | 0.03 | 0.03 |
| FA | =~ | un02 | 0.35 | -0.02 | -0.02 | -0.02 | -0.02 |
| FA | =~ | ut06 | 0.33 | 0.03 | 0.03 | 0.02 | 0.02 |
| FA | =~ | ut09 | 0.24 | -0.03 | -0.03 | -0.02 | -0.02 |
| FA | =~ | ut05 | 0.09 | 0.02 | 0.02 | 0.01 | 0.01 |
| FA | =~ | de09 | 0.09 | -0.02 | -0.02 | -0.02 | -0.02 |
| FA | =~ | un12 | 0.06 | -0.01 | -0.01 | -0.01 | -0.01 |
| FA | =~ | de08 | 0.03 | 0.01 | 0.01 | 0.01 | 0.01 |
| FA | =~ | de01 | 0.02 | 0.01 | 0.01 | 0.01 | 0.01 |
| FA | =~ | ut12 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| FA | =~ | pr01 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| FA | =~ | un09 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| PR | =~ | fa06 | 134.64 | 0.87 | 0.67 | 0.62 | 0.62 |
| PR | =~ | co03 | 77.18 | 0.74 | 0.57 | 0.56 | 0.56 |
| PR | =~ | fa02 | 73.45 | -0.77 | -0.59 | -0.50 | -0.50 |
| PR | =~ | ut11 | 62.90 | 0.83 | 0.64 | 0.52 | 0.52 |
| PR | =~ | co04 | 62.73 | 0.75 | 0.58 | 0.53 | 0.53 |
| PR | =~ | co08 | 59.54 | -0.70 | -0.54 | -0.52 | -0.52 |
| PR | =~ | fa08 | 41.01 | -0.56 | -0.43 | -0.38 | -0.38 |
| PR | =~ | fa09 | 34.42 | -0.51 | -0.40 | -0.34 | -0.34 |
| PR | =~ | ut01 | 34.08 | -0.42 | -0.32 | -0.31 | -0.31 |
| PR | =~ | co09 | 25.92 | -0.39 | -0.30 | -0.28 | -0.28 |
| PR | =~ | de05 | 23.16 | 1.12 | 0.87 | 0.74 | 0.74 |
| PR | =~ | un06 | 15.26 | -0.28 | -0.22 | -0.18 | -0.18 |
| PR | =~ | ut12 | 12.98 | 0.31 | 0.24 | 0.21 | 0.21 |
| PR | =~ | ut02 | 12.92 | -0.24 | -0.18 | -0.18 | -0.18 |
| PR | =~ | co05 | 11.98 | -0.26 | -0.20 | -0.18 | -0.18 |
| PR | =~ | fa04 | 11.62 | 0.29 | 0.22 | 0.20 | 0.20 |
| PR | =~ | de10 | 11.52 | -0.74 | -0.57 | -0.49 | -0.49 |
| PR | =~ | ut08 | 9.63 | 0.26 | 0.20 | 0.19 | 0.19 |
| PR | =~ | de09 | 7.51 | 0.71 | 0.54 | 0.45 | 0.45 |
| PR | =~ | co01 | 7.27 | 0.21 | 0.16 | 0.15 | 0.15 |
| PR | =~ | de03 | 6.68 | -0.60 | -0.46 | -0.39 | -0.39 |
| PR | =~ | fa10 | 6.31 | -0.21 | -0.16 | -0.13 | -0.13 |
| PR | =~ | ut03 | 4.84 | -0.21 | -0.16 | -0.15 | -0.15 |
| PR | =~ | un11 | 4.50 | -0.12 | -0.09 | -0.07 | -0.07 |
| PR | =~ | de07 | 4.43 | 0.41 | 0.31 | 0.31 | 0.31 |
| PR | =~ | un07 | 2.45 | 0.09 | 0.07 | 0.06 | 0.06 |
| PR | =~ | co02 | 2.40 | 0.12 | 0.09 | 0.09 | 0.09 |
| PR | =~ | un01 | 2.35 | 0.08 | 0.06 | 0.06 | 0.06 |
| PR | =~ | de08 | 2.21 | -0.29 | -0.23 | -0.21 | -0.21 |
| PR | =~ | ut07 | 1.97 | 0.13 | 0.10 | 0.09 | 0.09 |
| PR | =~ | co06 | 1.82 | -0.12 | -0.09 | -0.08 | -0.08 |
| PR | =~ | un12 | 1.79 | 0.07 | 0.06 | 0.05 | 0.05 |
| PR | =~ | un02 | 1.60 | -0.06 | -0.05 | -0.04 | -0.04 |
| PR | =~ | ut09 | 1.59 | 0.11 | 0.09 | 0.08 | 0.08 |
| PR | =~ | un05 | 1.38 | 0.06 | 0.04 | 0.04 | 0.04 |
| PR | =~ | fa05 | 1.25 | 0.07 | 0.06 | 0.05 | 0.05 |
| PR | =~ | ut06 | 1.20 | 0.08 | 0.07 | 0.06 | 0.06 |
| PR | =~ | un09 | 0.77 | 0.05 | 0.04 | 0.03 | 0.03 |
| PR | =~ | ut04 | 0.56 | -0.07 | -0.06 | -0.05 | -0.05 |
| PR | =~ | co10 | 0.41 | 0.05 | 0.04 | 0.04 | 0.04 |
| PR | =~ | de02 | 0.28 | -0.11 | -0.09 | -0.08 | -0.08 |
| PR | =~ | un04 | 0.20 | -0.02 | -0.02 | -0.02 | -0.02 |
| PR | =~ | un10 | 0.09 | -0.02 | -0.01 | -0.01 | -0.01 |
| PR | =~ | un08 | 0.08 | 0.02 | 0.01 | 0.01 | 0.01 |
| PR | =~ | un03 | 0.07 | 0.02 | 0.01 | 0.01 | 0.01 |
| PR | =~ | fa01 | 0.01 | 0.01 | 0.01 | 0.00 | 0.00 |
| PR | =~ | de06 | 0.00 | 0.01 | 0.01 | 0.01 | 0.01 |
| PR | =~ | de01 | 0.00 | 0.01 | 0.01 | 0.00 | 0.00 |
| PR | =~ | ut05 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| UN | =~ | fa02 | 56.03 | -0.51 | -0.40 | -0.34 | -0.34 |
| UN | =~ | fa08 | 47.24 | -0.45 | -0.35 | -0.31 | -0.31 |
| UN | =~ | fa09 | 36.97 | -0.40 | -0.31 | -0.26 | -0.26 |
| UN | =~ | fa06 | 28.05 | 0.30 | 0.23 | 0.21 | 0.21 |
| UN | =~ | co10 | 16.84 | 0.23 | 0.18 | 0.17 | 0.17 |
| UN | =~ | co08 | 14.61 | -0.24 | -0.18 | -0.18 | -0.18 |
| UN | =~ | ut11 | 11.47 | 0.23 | 0.18 | 0.14 | 0.14 |
| UN | =~ | ut03 | 10.84 | -0.20 | -0.16 | -0.14 | -0.14 |
| UN | =~ | de07 | 9.76 | 0.17 | 0.13 | 0.13 | 0.13 |
| UN | =~ | co04 | 8.55 | 0.19 | 0.15 | 0.14 | 0.14 |
| UN | =~ | de10 | 8.50 | -0.17 | -0.13 | -0.11 | -0.11 |
| UN | =~ | co05 | 7.66 | -0.13 | -0.11 | -0.09 | -0.09 |
| UN | =~ | co09 | 7.08 | -0.13 | -0.10 | -0.10 | -0.10 |
| UN | =~ | pr02 | 6.66 | 0.14 | 0.11 | 0.11 | 0.11 |
| UN | =~ | fa10 | 5.93 | -0.15 | -0.12 | -0.10 | -0.10 |
| UN | =~ | pr07 | 4.32 | -0.11 | -0.09 | -0.08 | -0.08 |
| UN | =~ | ut12 | 4.00 | 0.11 | 0.09 | 0.07 | 0.07 |
| UN | =~ | fa04 | 3.95 | 0.13 | 0.10 | 0.09 | 0.09 |
| UN | =~ | co03 | 3.50 | 0.11 | 0.08 | 0.08 | 0.08 |
| UN | =~ | de06 | 1.89 | 0.09 | 0.07 | 0.06 | 0.06 |
| UN | =~ | de08 | 1.65 | 0.07 | 0.05 | 0.05 | 0.05 |
| UN | =~ | de01 | 1.43 | 0.07 | 0.06 | 0.05 | 0.05 |
| UN | =~ | pr06 | 1.41 | 0.07 | 0.05 | 0.05 | 0.05 |
| UN | =~ | de05 | 1.18 | 0.07 | 0.06 | 0.05 | 0.05 |
| UN | =~ | co02 | 1.13 | 0.05 | 0.04 | 0.04 | 0.04 |
| UN | =~ | ut06 | 0.81 | -0.04 | -0.03 | -0.03 | -0.03 |
| UN | =~ | fa01 | 0.67 | 0.04 | 0.03 | 0.03 | 0.03 |
| UN | =~ | pr10 | 0.65 | 0.05 | 0.04 | 0.04 | 0.04 |
| UN | =~ | fa05 | 0.56 | 0.03 | 0.03 | 0.02 | 0.02 |
| UN | =~ | pr05 | 0.55 | 0.05 | 0.04 | 0.03 | 0.03 |
| UN | =~ | co06 | 0.53 | 0.04 | 0.03 | 0.03 | 0.03 |
| UN | =~ | pr08 | 0.50 | -0.03 | -0.02 | -0.03 | -0.03 |
| UN | =~ | de02 | 0.50 | -0.04 | -0.03 | -0.03 | -0.03 |
| UN | =~ | ut05 | 0.40 | 0.04 | 0.03 | 0.02 | 0.02 |
| UN | =~ | ut04 | 0.37 | -0.04 | -0.03 | -0.03 | -0.03 |
| UN | =~ | de09 | 0.37 | -0.05 | -0.04 | -0.03 | -0.03 |
| UN | =~ | ut01 | 0.33 | -0.03 | -0.02 | -0.02 | -0.02 |
| UN | =~ | de03 | 0.27 | -0.03 | -0.03 | -0.02 | -0.02 |
| UN | =~ | ut07 | 0.27 | -0.03 | -0.02 | -0.02 | -0.02 |
| UN | =~ | pr01 | 0.11 | 0.01 | 0.01 | 0.01 | 0.01 |
| UN | =~ | co01 | 0.10 | 0.02 | 0.01 | 0.01 | 0.01 |
| UN | =~ | ut02 | 0.07 | -0.01 | -0.01 | -0.01 | -0.01 |
| UN | =~ | pr09 | 0.02 | -0.01 | -0.01 | -0.01 | -0.01 |
| UN | =~ | ut08 | 0.01 | 0.01 | 0.00 | 0.00 | 0.00 |
| UN | =~ | ut09 | 0.01 | 0.00 | 0.00 | 0.00 | 0.00 |
| UT | =~ | co04 | 112.78 | 0.78 | 0.63 | 0.58 | 0.58 |
| UT | =~ | co03 | 71.66 | 0.55 | 0.44 | 0.43 | 0.43 |
| UT | =~ | co08 | 58.36 | -0.54 | -0.44 | -0.42 | -0.42 |
| UT | =~ | de05 | 52.89 | 0.67 | 0.54 | 0.47 | 0.47 |
| UT | =~ | fa06 | 47.51 | 0.42 | 0.34 | 0.32 | 0.32 |
| UT | =~ | fa02 | 37.75 | -0.46 | -0.37 | -0.31 | -0.31 |
| UT | =~ | co09 | 29.78 | -0.31 | -0.26 | -0.24 | -0.24 |
| UT | =~ | fa08 | 28.61 | -0.38 | -0.31 | -0.28 | -0.28 |
| UT | =~ | de02 | 20.41 | -0.36 | -0.30 | -0.26 | -0.26 |
| UT | =~ | co05 | 19.51 | -0.25 | -0.20 | -0.18 | -0.18 |
| UT | =~ | fa09 | 19.11 | -0.31 | -0.26 | -0.22 | -0.22 |
| UT | =~ | pr06 | 18.86 | 0.36 | 0.29 | 0.28 | 0.28 |
| UT | =~ | de09 | 17.71 | 0.43 | 0.35 | 0.29 | 0.29 |
| UT | =~ | un01 | 16.59 | 0.18 | 0.15 | 0.14 | 0.14 |
| UT | =~ | de07 | 14.55 | 0.29 | 0.23 | 0.23 | 0.23 |
| UT | =~ | un06 | 13.77 | -0.25 | -0.20 | -0.16 | -0.16 |
| UT | =~ | un10 | 10.65 | -0.17 | -0.14 | -0.12 | -0.12 |
| UT | =~ | un12 | 8.85 | 0.14 | 0.12 | 0.10 | 0.10 |
| UT | =~ | de03 | 7.09 | -0.24 | -0.19 | -0.16 | -0.16 |
| UT | =~ | un11 | 6.12 | -0.12 | -0.10 | -0.08 | -0.08 |
| UT | =~ | un03 | 5.78 | 0.15 | 0.12 | 0.10 | 0.10 |
| UT | =~ | de10 | 5.42 | -0.19 | -0.15 | -0.13 | -0.13 |
| UT | =~ | pr02 | 4.21 | -0.15 | -0.13 | -0.13 | -0.13 |
| UT | =~ | pr08 | 3.96 | 0.13 | 0.10 | 0.11 | 0.11 |
| UT | =~ | pr01 | 3.87 | 0.13 | 0.10 | 0.10 | 0.10 |
| UT | =~ | co01 | 3.54 | 0.11 | 0.09 | 0.08 | 0.08 |
| UT | =~ | fa04 | 3.16 | -0.12 | -0.10 | -0.09 | -0.09 |
| UT | =~ | un02 | 2.69 | -0.07 | -0.06 | -0.05 | -0.05 |
| UT | =~ | un05 | 2.66 | 0.07 | 0.06 | 0.05 | 0.05 |
| UT | =~ | de06 | 1.82 | -0.12 | -0.10 | -0.08 | -0.08 |
| UT | =~ | un08 | 1.31 | 0.06 | 0.05 | 0.04 | 0.04 |
| UT | =~ | pr05 | 1.12 | -0.10 | -0.08 | -0.07 | -0.07 |
| UT | =~ | fa01 | 0.93 | 0.05 | 0.04 | 0.04 | 0.04 |
| UT | =~ | pr10 | 0.86 | 0.08 | 0.06 | 0.06 | 0.06 |
| UT | =~ | un07 | 0.64 | -0.04 | -0.03 | -0.03 | -0.03 |
| UT | =~ | fa05 | 0.60 | 0.04 | 0.03 | 0.03 | 0.03 |
| UT | =~ | un09 | 0.49 | -0.04 | -0.03 | -0.03 | -0.03 |
| UT | =~ | co02 | 0.47 | -0.04 | -0.03 | -0.03 | -0.03 |
| UT | =~ | de01 | 0.40 | -0.05 | -0.04 | -0.04 | -0.04 |
| UT | =~ | fa10 | 0.35 | 0.04 | 0.03 | 0.03 | 0.03 |
| UT | =~ | pr07 | 0.27 | -0.04 | -0.03 | -0.03 | -0.03 |
| UT | =~ | un04 | 0.24 | -0.02 | -0.02 | -0.02 | -0.02 |
| UT | =~ | co06 | 0.23 | -0.03 | -0.03 | -0.02 | -0.02 |
| UT | =~ | de08 | 0.20 | -0.03 | -0.03 | -0.03 | -0.03 |
| UT | =~ | pr09 | 0.10 | -0.02 | -0.02 | -0.02 | -0.02 |
| UT | =~ | co10 | 0.01 | 0.00 | 0.00 | 0.00 | 0.00 |
Items correlations:
modif3 %>%
filter(op == "~~" &
!(lhs %in% c("PR", "CO", "UT", "FA", "DE", "UN")) &
!(rhs %in% c("PR", "CO", "UT", "FA", "DE", "UN"))) %>%
kable(digits = 2,
col.names = c("Item", "", "Item", "Modification Index", "epc", "sepc.lv", "sepc.all", "sepc.nox"))| Item | Item | Modification Index | epc | sepc.lv | sepc.all | sepc.nox | |
|---|---|---|---|---|---|---|---|
| de05 | ~~ | de09 | 182.77 | 0.70 | 0.70 | 0.63 | 0.63 |
| pr05 | ~~ | de06 | 168.01 | 0.58 | 0.58 | 0.62 | 0.62 |
| fa02 | ~~ | fa08 | 132.56 | 0.60 | 0.60 | 0.53 | 0.53 |
| pr05 | ~~ | ut11 | 125.45 | 0.54 | 0.54 | 0.52 | 0.52 |
| fa02 | ~~ | fa09 | 118.25 | 0.57 | 0.57 | 0.51 | 0.51 |
| fa08 | ~~ | fa09 | 105.17 | 0.52 | 0.52 | 0.48 | 0.48 |
| ut11 | ~~ | de06 | 100.33 | 0.47 | 0.47 | 0.47 | 0.47 |
| ut03 | ~~ | de09 | 59.71 | 0.39 | 0.39 | 0.35 | 0.35 |
| pr06 | ~~ | ut06 | 58.50 | 0.24 | 0.24 | 0.37 | 0.37 |
| ut01 | ~~ | ut02 | 55.84 | 0.17 | 0.17 | 0.44 | 0.44 |
| fa02 | ~~ | fa06 | 51.07 | -0.32 | -0.32 | -0.34 | -0.34 |
| co08 | ~~ | ut03 | 44.59 | -0.29 | -0.29 | -0.31 | -0.31 |
| co08 | ~~ | co09 | 43.60 | 0.24 | 0.24 | 0.33 | 0.33 |
| ut03 | ~~ | fa04 | 41.73 | -0.29 | -0.29 | -0.30 | -0.30 |
| co04 | ~~ | co08 | 41.47 | -0.29 | -0.29 | -0.29 | -0.29 |
| ut07 | ~~ | ut08 | 40.80 | 0.23 | 0.23 | 0.30 | 0.30 |
| co08 | ~~ | de09 | 40.13 | -0.32 | -0.32 | -0.29 | -0.29 |
| co03 | ~~ | co04 | 37.93 | 0.26 | 0.26 | 0.29 | 0.29 |
| co04 | ~~ | de10 | 37.10 | -0.25 | -0.25 | -0.29 | -0.29 |
| ut06 | ~~ | de07 | 36.79 | 0.17 | 0.17 | 0.30 | 0.30 |
| pr06 | ~~ | de07 | 35.62 | 0.20 | 0.20 | 0.28 | 0.28 |
| co05 | ~~ | co09 | 33.35 | 0.18 | 0.18 | 0.34 | 0.34 |
| fa04 | ~~ | un07 | 32.68 | 0.23 | 0.23 | 0.27 | 0.27 |
| un01 | ~~ | un08 | 32.62 | 0.15 | 0.15 | 0.29 | 0.29 |
| co04 | ~~ | un01 | 29.62 | 0.18 | 0.18 | 0.26 | 0.26 |
| pr08 | ~~ | de05 | 29.56 | 0.16 | 0.16 | 0.26 | 0.26 |
| pr09 | ~~ | de10 | 26.09 | -0.16 | -0.16 | -0.25 | -0.25 |
| fa04 | ~~ | de05 | 25.91 | -0.24 | -0.24 | -0.24 | -0.24 |
| fa09 | ~~ | un03 | 24.74 | -0.24 | -0.24 | -0.23 | -0.23 |
| pr08 | ~~ | de06 | 24.58 | -0.15 | -0.15 | -0.24 | -0.24 |
| pr09 | ~~ | ut08 | 24.00 | 0.15 | 0.15 | 0.23 | 0.23 |
| co08 | ~~ | fa04 | 23.88 | 0.22 | 0.22 | 0.22 | 0.22 |
| fa04 | ~~ | de03 | 23.44 | 0.22 | 0.22 | 0.23 | 0.23 |
| fa04 | ~~ | un10 | 23.32 | 0.19 | 0.19 | 0.23 | 0.23 |
| co08 | ~~ | un07 | 22.48 | 0.19 | 0.19 | 0.22 | 0.22 |
| co10 | ~~ | ut03 | 22.37 | -0.19 | -0.19 | -0.22 | -0.22 |
| co03 | ~~ | co08 | 21.95 | -0.19 | -0.19 | -0.22 | -0.22 |
| co08 | ~~ | un01 | 21.84 | -0.15 | -0.15 | -0.22 | -0.22 |
| fa02 | ~~ | fa10 | 21.72 | 0.23 | 0.23 | 0.22 | 0.22 |
| fa04 | ~~ | fa08 | 21.49 | -0.23 | -0.23 | -0.21 | -0.21 |
| fa06 | ~~ | fa08 | 21.28 | -0.20 | -0.20 | -0.22 | -0.22 |
| co03 | ~~ | un10 | 21.24 | -0.16 | -0.16 | -0.22 | -0.22 |
| ut02 | ~~ | un10 | 21.21 | -0.11 | -0.11 | -0.24 | -0.24 |
| fa06 | ~~ | fa10 | 21.14 | -0.19 | -0.19 | -0.23 | -0.23 |
| fa04 | ~~ | de09 | 21.00 | -0.24 | -0.24 | -0.21 | -0.21 |
| de05 | ~~ | un07 | 20.82 | -0.18 | -0.18 | -0.22 | -0.22 |
| fa04 | ~~ | fa09 | 20.42 | -0.22 | -0.22 | -0.21 | -0.21 |
| co04 | ~~ | co05 | 20.40 | -0.16 | -0.16 | -0.23 | -0.23 |
| fa04 | ~~ | un01 | 19.90 | -0.15 | -0.15 | -0.21 | -0.21 |
| co08 | ~~ | de10 | 19.79 | 0.17 | 0.17 | 0.21 | 0.21 |
| fa09 | ~~ | un06 | 19.23 | 0.23 | 0.23 | 0.20 | 0.20 |
| co04 | ~~ | ut01 | 18.96 | 0.14 | 0.14 | 0.21 | 0.21 |
| pr07 | ~~ | de02 | 18.78 | 0.14 | 0.14 | 0.22 | 0.22 |
| de05 | ~~ | un05 | 18.63 | 0.14 | 0.14 | 0.21 | 0.21 |
| co03 | ~~ | de07 | 18.33 | 0.14 | 0.14 | 0.20 | 0.20 |
| fa02 | ~~ | un03 | 18.30 | -0.21 | -0.21 | -0.20 | -0.20 |
| co09 | ~~ | de05 | 18.28 | -0.15 | -0.15 | -0.21 | -0.21 |
| fa04 | ~~ | un12 | 18.25 | -0.16 | -0.16 | -0.20 | -0.20 |
| ut03 | ~~ | fa09 | 18.22 | 0.20 | 0.20 | 0.20 | 0.20 |
| co04 | ~~ | un07 | 18.15 | -0.17 | -0.17 | -0.20 | -0.20 |
| fa06 | ~~ | fa09 | 18.03 | -0.18 | -0.18 | -0.20 | -0.20 |
| ut01 | ~~ | de05 | 18.00 | 0.14 | 0.14 | 0.21 | 0.21 |
| co04 | ~~ | de05 | 17.94 | 0.20 | 0.20 | 0.20 | 0.20 |
| ut11 | ~~ | un09 | 17.94 | 0.19 | 0.19 | 0.20 | 0.20 |
| co05 | ~~ | co08 | 17.89 | 0.15 | 0.15 | 0.22 | 0.22 |
| co04 | ~~ | co09 | 17.73 | -0.16 | -0.16 | -0.21 | -0.21 |
| de09 | ~~ | un07 | 17.50 | -0.19 | -0.19 | -0.19 | -0.19 |
| de05 | ~~ | un10 | 17.02 | -0.16 | -0.16 | -0.20 | -0.20 |
| fa04 | ~~ | un08 | 16.99 | -0.15 | -0.15 | -0.20 | -0.20 |
| de05 | ~~ | un12 | 16.99 | 0.15 | 0.15 | 0.20 | 0.20 |
| co08 | ~~ | fa09 | 16.13 | -0.19 | -0.19 | -0.19 | -0.19 |
| fa08 | ~~ | un03 | 15.93 | -0.19 | -0.19 | -0.18 | -0.18 |
| ut01 | ~~ | fa04 | 15.86 | -0.13 | -0.13 | -0.19 | -0.19 |
| co09 | ~~ | fa04 | 15.60 | 0.14 | 0.14 | 0.19 | 0.19 |
| pr01 | ~~ | un01 | 15.38 | 0.09 | 0.09 | 0.20 | 0.20 |
| fa10 | ~~ | un08 | 15.35 | 0.14 | 0.14 | 0.19 | 0.19 |
| pr05 | ~~ | de01 | 15.31 | -0.16 | -0.16 | -0.18 | -0.18 |
| ut01 | ~~ | un07 | 15.31 | -0.11 | -0.11 | -0.19 | -0.19 |
| co04 | ~~ | ut03 | 15.13 | 0.18 | 0.18 | 0.18 | 0.18 |
| ut01 | ~~ | ut03 | 15.09 | 0.12 | 0.12 | 0.19 | 0.19 |
| co04 | ~~ | fa04 | 15.01 | -0.18 | -0.18 | -0.18 | -0.18 |
| co06 | ~~ | un10 | 14.83 | 0.13 | 0.13 | 0.19 | 0.19 |
| un07 | ~~ | un09 | 14.63 | 0.14 | 0.14 | 0.18 | 0.18 |
| ut03 | ~~ | de05 | 14.57 | 0.17 | 0.17 | 0.18 | 0.18 |
| co08 | ~~ | de05 | 14.56 | -0.17 | -0.17 | -0.18 | -0.18 |
| de01 | ~~ | de09 | 14.39 | -0.18 | -0.18 | -0.18 | -0.18 |
| un01 | ~~ | un10 | 14.28 | -0.11 | -0.11 | -0.19 | -0.19 |
| co04 | ~~ | ut04 | 14.14 | 0.17 | 0.17 | 0.17 | 0.17 |
| fa02 | ~~ | fa04 | 14.14 | -0.19 | -0.19 | -0.17 | -0.17 |
| un07 | ~~ | un10 | 14.10 | 0.13 | 0.13 | 0.18 | 0.18 |
| pr09 | ~~ | de01 | 13.97 | 0.12 | 0.12 | 0.18 | 0.18 |
| fa10 | ~~ | un07 | 13.93 | -0.15 | -0.15 | -0.18 | -0.18 |
| fa04 | ~~ | de02 | 13.83 | 0.15 | 0.15 | 0.18 | 0.18 |
| pr08 | ~~ | de09 | 13.70 | 0.13 | 0.13 | 0.18 | 0.18 |
| fa09 | ~~ | de09 | 13.29 | 0.20 | 0.20 | 0.17 | 0.17 |
| pr08 | ~~ | co03 | 13.19 | 0.10 | 0.10 | 0.18 | 0.18 |
| pr05 | ~~ | de05 | 13.00 | -0.16 | -0.16 | -0.17 | -0.17 |
| ut03 | ~~ | fa08 | 12.94 | 0.17 | 0.17 | 0.17 | 0.17 |
| fa06 | ~~ | un08 | 12.84 | -0.12 | -0.12 | -0.17 | -0.17 |
| de02 | ~~ | un01 | 12.71 | -0.10 | -0.10 | -0.18 | -0.18 |
| fa04 | ~~ | de10 | 12.65 | 0.14 | 0.14 | 0.17 | 0.17 |
| de03 | ~~ | de10 | 12.61 | 0.14 | 0.14 | 0.18 | 0.18 |
| ut01 | ~~ | un09 | 12.54 | -0.10 | -0.10 | -0.18 | -0.18 |
| ut03 | ~~ | un10 | 12.50 | -0.13 | -0.13 | -0.17 | -0.17 |
| co06 | ~~ | un01 | 12.38 | -0.11 | -0.11 | -0.17 | -0.17 |
| co01 | ~~ | co06 | 12.28 | -0.12 | -0.12 | -0.19 | -0.19 |
| pr09 | ~~ | ut12 | 12.14 | 0.11 | 0.11 | 0.17 | 0.17 |
| de03 | ~~ | de05 | 12.11 | -0.16 | -0.16 | -0.17 | -0.17 |
| de01 | ~~ | de06 | 12.08 | -0.15 | -0.15 | -0.17 | -0.17 |
| ut01 | ~~ | ut11 | 12.07 | -0.12 | -0.12 | -0.17 | -0.17 |
| fa09 | ~~ | un07 | 11.93 | -0.14 | -0.14 | -0.16 | -0.16 |
| co04 | ~~ | un10 | 11.92 | -0.13 | -0.13 | -0.16 | -0.16 |
| co08 | ~~ | un10 | 11.81 | 0.13 | 0.13 | 0.16 | 0.16 |
| de03 | ~~ | de09 | 11.78 | -0.17 | -0.17 | -0.16 | -0.16 |
| fa09 | ~~ | fa10 | 11.75 | 0.16 | 0.16 | 0.16 | 0.16 |
| de09 | ~~ | de10 | 11.64 | -0.16 | -0.16 | -0.16 | -0.16 |
| ut05 | ~~ | un10 | 11.63 | 0.12 | 0.12 | 0.16 | 0.16 |
| co04 | ~~ | un05 | 11.63 | 0.11 | 0.11 | 0.17 | 0.17 |
| co04 | ~~ | de09 | 11.55 | 0.18 | 0.18 | 0.15 | 0.15 |
| un01 | ~~ | un05 | 11.54 | 0.08 | 0.08 | 0.18 | 0.18 |
| de05 | ~~ | de10 | 11.50 | -0.14 | -0.14 | -0.17 | -0.17 |
| ut02 | ~~ | un12 | 11.44 | 0.08 | 0.08 | 0.18 | 0.18 |
| pr05 | ~~ | un01 | 11.41 | -0.11 | -0.11 | -0.16 | -0.16 |
| co06 | ~~ | fa04 | 11.20 | 0.14 | 0.14 | 0.16 | 0.16 |
| pr08 | ~~ | ut02 | 11.18 | 0.06 | 0.06 | 0.18 | 0.18 |
| co10 | ~~ | fa08 | 11.16 | -0.14 | -0.14 | -0.16 | -0.16 |
| un02 | ~~ | un06 | 11.11 | 0.11 | 0.11 | 0.17 | 0.17 |
| pr01 | ~~ | un04 | 11.08 | 0.08 | 0.08 | 0.17 | 0.17 |
| co10 | ~~ | un10 | 11.07 | 0.11 | 0.11 | 0.16 | 0.16 |
| pr01 | ~~ | co08 | 11.01 | -0.10 | -0.10 | -0.16 | -0.16 |
| co05 | ~~ | ut01 | 10.94 | -0.08 | -0.08 | -0.18 | -0.18 |
| pr01 | ~~ | de10 | 10.93 | -0.09 | -0.09 | -0.17 | -0.17 |
| de09 | ~~ | un06 | 10.79 | 0.18 | 0.18 | 0.15 | 0.15 |
| ut02 | ~~ | ut03 | 10.62 | 0.10 | 0.10 | 0.17 | 0.17 |
| ut03 | ~~ | fa02 | 10.62 | 0.16 | 0.16 | 0.15 | 0.15 |
| de09 | ~~ | un05 | 10.57 | 0.12 | 0.12 | 0.16 | 0.16 |
| pr07 | ~~ | ut09 | 10.47 | 0.11 | 0.11 | 0.16 | 0.16 |
| ut08 | ~~ | de01 | 10.41 | 0.11 | 0.11 | 0.15 | 0.15 |
| pr01 | ~~ | pr09 | 10.40 | 0.08 | 0.08 | 0.17 | 0.17 |
| co09 | ~~ | ut03 | 10.15 | -0.11 | -0.11 | -0.16 | -0.16 |
| pr01 | ~~ | ut03 | 10.12 | 0.09 | 0.09 | 0.16 | 0.16 |
| de10 | ~~ | un08 | 10.11 | -0.10 | -0.10 | -0.16 | -0.16 |
| fa06 | ~~ | de02 | 10.07 | 0.11 | 0.11 | 0.16 | 0.16 |
| fa08 | ~~ | fa10 | 10.03 | 0.15 | 0.15 | 0.15 | 0.15 |
| un05 | ~~ | un09 | 9.91 | -0.09 | -0.09 | -0.16 | -0.16 |
| pr07 | ~~ | de06 | 9.90 | -0.11 | -0.11 | -0.16 | -0.16 |
| ut03 | ~~ | un06 | 9.84 | 0.15 | 0.15 | 0.14 | 0.14 |
| ut02 | ~~ | de05 | 9.82 | 0.09 | 0.09 | 0.16 | 0.16 |
| ut01 | ~~ | fa10 | 9.66 | 0.10 | 0.10 | 0.16 | 0.16 |
| co06 | ~~ | un09 | 9.62 | 0.12 | 0.12 | 0.15 | 0.15 |
| ut08 | ~~ | ut11 | 9.59 | -0.13 | -0.13 | -0.15 | -0.15 |
| un08 | ~~ | un11 | 9.53 | 0.09 | 0.09 | 0.16 | 0.16 |
| co08 | ~~ | un05 | 9.50 | -0.09 | -0.09 | -0.15 | -0.15 |
| pr09 | ~~ | un11 | 9.49 | -0.09 | -0.09 | -0.15 | -0.15 |
| de08 | ~~ | un04 | 9.36 | -0.08 | -0.08 | -0.15 | -0.15 |
| fa06 | ~~ | un10 | 9.21 | 0.10 | 0.10 | 0.15 | 0.15 |
| fa02 | ~~ | un06 | 9.20 | 0.16 | 0.16 | 0.14 | 0.14 |
| pr06 | ~~ | de03 | 9.08 | -0.12 | -0.12 | -0.14 | -0.14 |
| co05 | ~~ | un04 | 9.05 | 0.08 | 0.08 | 0.16 | 0.16 |
| pr06 | ~~ | un08 | 8.90 | -0.10 | -0.10 | -0.14 | -0.14 |
| de02 | ~~ | de05 | 8.90 | -0.12 | -0.12 | -0.15 | -0.15 |
| co04 | ~~ | un12 | 8.90 | 0.11 | 0.11 | 0.14 | 0.14 |
| fa04 | ~~ | fa06 | 8.87 | 0.13 | 0.13 | 0.14 | 0.14 |
| de10 | ~~ | un01 | 8.79 | -0.09 | -0.09 | -0.15 | -0.15 |
| fa05 | ~~ | de09 | 8.77 | -0.11 | -0.11 | -0.16 | -0.16 |
| pr05 | ~~ | un09 | 8.75 | 0.12 | 0.12 | 0.14 | 0.14 |
| un02 | ~~ | un12 | 8.74 | -0.07 | -0.07 | -0.16 | -0.16 |
| co04 | ~~ | de02 | 8.58 | -0.12 | -0.12 | -0.14 | -0.14 |
| co03 | ~~ | co05 | 8.49 | -0.09 | -0.09 | -0.15 | -0.15 |
| ut02 | ~~ | ut12 | 8.49 | -0.08 | -0.08 | -0.16 | -0.16 |
| de09 | ~~ | un10 | 8.47 | -0.13 | -0.13 | -0.14 | -0.14 |
| de05 | ~~ | un08 | 8.44 | 0.10 | 0.10 | 0.14 | 0.14 |
| co06 | ~~ | ut09 | 8.43 | 0.11 | 0.11 | 0.14 | 0.14 |
| pr01 | ~~ | fa06 | 8.37 | 0.08 | 0.08 | 0.15 | 0.15 |
| pr06 | ~~ | de02 | 8.31 | -0.10 | -0.10 | -0.14 | -0.14 |
| pr01 | ~~ | fa04 | 8.29 | -0.09 | -0.09 | -0.14 | -0.14 |
| co08 | ~~ | ut02 | 8.29 | -0.08 | -0.08 | -0.15 | -0.15 |
| pr08 | ~~ | un07 | 8.25 | -0.08 | -0.08 | -0.14 | -0.14 |
| pr09 | ~~ | un05 | 8.21 | 0.07 | 0.07 | 0.14 | 0.14 |
| ut01 | ~~ | ut09 | 8.19 | -0.09 | -0.09 | -0.15 | -0.15 |
| fa04 | ~~ | fa10 | 8.19 | -0.13 | -0.13 | -0.14 | -0.14 |
| co03 | ~~ | fa04 | 8.17 | -0.12 | -0.12 | -0.13 | -0.13 |
| pr05 | ~~ | ut12 | 8.15 | -0.11 | -0.11 | -0.14 | -0.14 |
| de06 | ~~ | de10 | 8.14 | 0.11 | 0.11 | 0.14 | 0.14 |
| co03 | ~~ | de03 | 8.12 | -0.11 | -0.11 | -0.13 | -0.13 |
| co03 | ~~ | ut03 | 8.10 | 0.11 | 0.11 | 0.13 | 0.13 |
| pr08 | ~~ | de03 | 8.04 | -0.08 | -0.08 | -0.14 | -0.14 |
| pr05 | ~~ | de10 | 7.93 | 0.11 | 0.11 | 0.14 | 0.14 |
| co09 | ~~ | un07 | 7.88 | 0.09 | 0.09 | 0.14 | 0.14 |
| ut11 | ~~ | fa06 | 7.81 | 0.12 | 0.12 | 0.13 | 0.13 |
| fa01 | ~~ | un01 | 7.75 | 0.07 | 0.07 | 0.15 | 0.15 |
| un01 | ~~ | un07 | 7.72 | -0.08 | -0.08 | -0.14 | -0.14 |
| co04 | ~~ | un08 | 7.69 | 0.10 | 0.10 | 0.13 | 0.13 |
| ut09 | ~~ | un09 | 7.63 | 0.10 | 0.10 | 0.13 | 0.13 |
| fa02 | ~~ | un10 | 7.62 | -0.11 | -0.11 | -0.13 | -0.13 |
| fa01 | ~~ | un10 | 7.62 | -0.07 | -0.07 | -0.15 | -0.15 |
| de03 | ~~ | un10 | 7.62 | 0.10 | 0.10 | 0.13 | 0.13 |
| pr01 | ~~ | pr05 | 7.61 | -0.09 | -0.09 | -0.14 | -0.14 |
| ut01 | ~~ | un01 | 7.60 | 0.06 | 0.06 | 0.14 | 0.14 |
| fa10 | ~~ | un01 | 7.58 | 0.09 | 0.09 | 0.13 | 0.13 |
| ut11 | ~~ | de05 | 7.58 | -0.13 | -0.13 | -0.13 | -0.13 |
| pr05 | ~~ | ut01 | 7.56 | -0.09 | -0.09 | -0.14 | -0.14 |
| un02 | ~~ | un03 | 7.43 | 0.08 | 0.08 | 0.14 | 0.14 |
| fa10 | ~~ | de02 | 7.43 | -0.11 | -0.11 | -0.13 | -0.13 |
| ut01 | ~~ | fa06 | 7.42 | -0.08 | -0.08 | -0.14 | -0.14 |
| de03 | ~~ | de06 | 7.40 | 0.12 | 0.12 | 0.13 | 0.13 |
| ut02 | ~~ | fa04 | 7.36 | -0.08 | -0.08 | -0.14 | -0.14 |
| ut04 | ~~ | ut07 | 7.36 | -0.12 | -0.12 | -0.13 | -0.13 |
| co09 | ~~ | un12 | 7.27 | -0.08 | -0.08 | -0.14 | -0.14 |
| co09 | ~~ | un06 | 7.24 | 0.10 | 0.10 | 0.13 | 0.13 |
| un02 | ~~ | un04 | 7.23 | 0.07 | 0.07 | 0.14 | 0.14 |
| ut01 | ~~ | ut05 | 7.15 | 0.08 | 0.08 | 0.14 | 0.14 |
| co01 | ~~ | ut12 | 7.07 | 0.08 | 0.08 | 0.13 | 0.13 |
| ut07 | ~~ | de06 | 7.05 | -0.11 | -0.11 | -0.13 | -0.13 |
| pr09 | ~~ | fa04 | 7.03 | -0.10 | -0.10 | -0.12 | -0.12 |
| co03 | ~~ | un08 | 6.77 | 0.08 | 0.08 | 0.12 | 0.12 |
| co03 | ~~ | ut11 | 6.76 | 0.11 | 0.11 | 0.12 | 0.12 |
| pr09 | ~~ | ut11 | 6.75 | -0.10 | -0.10 | -0.12 | -0.12 |
| co04 | ~~ | un06 | 6.74 | -0.13 | -0.13 | -0.12 | -0.12 |
| pr08 | ~~ | fa08 | 6.71 | -0.08 | -0.08 | -0.12 | -0.12 |
| pr02 | ~~ | ut03 | 6.69 | -0.09 | -0.09 | -0.12 | -0.12 |
| ut11 | ~~ | de01 | 6.69 | -0.12 | -0.12 | -0.12 | -0.12 |
| pr06 | ~~ | co04 | 6.67 | 0.11 | 0.11 | 0.12 | 0.12 |
| fa05 | ~~ | fa09 | 6.64 | -0.09 | -0.09 | -0.15 | -0.15 |
| pr02 | ~~ | de06 | 6.61 | 0.09 | 0.09 | 0.12 | 0.12 |
| co01 | ~~ | de10 | 6.59 | 0.08 | 0.08 | 0.13 | 0.13 |
| de10 | ~~ | un09 | 6.56 | 0.09 | 0.09 | 0.13 | 0.13 |
| ut11 | ~~ | fa04 | 6.52 | 0.13 | 0.13 | 0.12 | 0.12 |
| co04 | ~~ | de07 | 6.50 | 0.10 | 0.10 | 0.12 | 0.12 |
| co08 | ~~ | ut05 | 6.49 | 0.11 | 0.11 | 0.12 | 0.12 |
| fa04 | ~~ | un09 | 6.44 | 0.11 | 0.11 | 0.12 | 0.12 |
| pr08 | ~~ | un02 | 6.42 | -0.05 | -0.05 | -0.13 | -0.13 |
| ut02 | ~~ | ut04 | 6.37 | 0.07 | 0.07 | 0.13 | 0.13 |
| un05 | ~~ | un07 | 6.33 | -0.07 | -0.07 | -0.13 | -0.13 |
| ut12 | ~~ | de03 | 6.33 | 0.10 | 0.10 | 0.12 | 0.12 |
| ut02 | ~~ | un03 | 6.32 | 0.07 | 0.07 | 0.13 | 0.13 |
| co05 | ~~ | de05 | 6.26 | -0.09 | -0.09 | -0.13 | -0.13 |
| co09 | ~~ | de06 | 6.25 | 0.09 | 0.09 | 0.13 | 0.13 |
| pr06 | ~~ | ut11 | 6.25 | -0.11 | -0.11 | -0.12 | -0.12 |
| fa10 | ~~ | un02 | 6.23 | -0.08 | -0.08 | -0.13 | -0.13 |
| co03 | ~~ | de05 | 6.17 | 0.10 | 0.10 | 0.12 | 0.12 |
| ut01 | ~~ | de02 | 6.10 | -0.07 | -0.07 | -0.13 | -0.13 |
| pr09 | ~~ | ut07 | 6.07 | 0.08 | 0.08 | 0.12 | 0.12 |
| fa08 | ~~ | de08 | 6.04 | 0.09 | 0.09 | 0.12 | 0.12 |
| pr05 | ~~ | co04 | 6.03 | -0.11 | -0.11 | -0.11 | -0.11 |
| ut06 | ~~ | de02 | 6.03 | -0.07 | -0.07 | -0.12 | -0.12 |
| fa08 | ~~ | un06 | 6.00 | 0.13 | 0.13 | 0.11 | 0.11 |
| fa09 | ~~ | de07 | 6.00 | -0.10 | -0.10 | -0.12 | -0.12 |
| de03 | ~~ | un12 | 5.96 | -0.09 | -0.09 | -0.12 | -0.12 |
| co08 | ~~ | un11 | 5.95 | 0.09 | 0.09 | 0.12 | 0.12 |
| co02 | ~~ | ut09 | 5.93 | -0.08 | -0.08 | -0.12 | -0.12 |
| ut07 | ~~ | ut09 | 5.92 | -0.10 | -0.10 | -0.12 | -0.12 |
| co03 | ~~ | de08 | 5.92 | 0.08 | 0.08 | 0.12 | 0.12 |
| pr05 | ~~ | ut08 | 5.91 | -0.09 | -0.09 | -0.12 | -0.12 |
| pr01 | ~~ | co04 | 5.85 | 0.07 | 0.07 | 0.12 | 0.12 |
| de06 | ~~ | un01 | 5.84 | -0.08 | -0.08 | -0.12 | -0.12 |
| co08 | ~~ | de03 | 5.81 | 0.10 | 0.10 | 0.11 | 0.11 |
| de03 | ~~ | un02 | 5.79 | 0.07 | 0.07 | 0.12 | 0.12 |
| co06 | ~~ | ut02 | 5.78 | -0.06 | -0.06 | -0.13 | -0.13 |
| de06 | ~~ | un09 | 5.76 | 0.10 | 0.10 | 0.12 | 0.12 |
| ut03 | ~~ | un07 | 5.74 | -0.09 | -0.09 | -0.11 | -0.11 |
| co01 | ~~ | un01 | 5.73 | 0.06 | 0.06 | 0.12 | 0.12 |
| ut01 | ~~ | un05 | 5.71 | 0.05 | 0.05 | 0.12 | 0.12 |
| ut03 | ~~ | de01 | 5.70 | -0.10 | -0.10 | -0.11 | -0.11 |
| pr05 | ~~ | fa08 | 5.68 | 0.11 | 0.11 | 0.11 | 0.11 |
| pr10 | ~~ | ut04 | 5.68 | -0.10 | -0.10 | -0.11 | -0.11 |
| ut05 | ~~ | de10 | 5.68 | 0.09 | 0.09 | 0.12 | 0.12 |
| un08 | ~~ | un10 | 5.67 | -0.07 | -0.07 | -0.12 | -0.12 |
| de05 | ~~ | un11 | 5.66 | -0.09 | -0.09 | -0.11 | -0.11 |
| de05 | ~~ | un06 | 5.66 | -0.12 | -0.12 | -0.11 | -0.11 |
| ut02 | ~~ | un07 | 5.64 | -0.06 | -0.06 | -0.12 | -0.12 |
| un08 | ~~ | un09 | 5.63 | -0.08 | -0.08 | -0.12 | -0.12 |
| co09 | ~~ | de02 | 5.61 | 0.07 | 0.07 | 0.12 | 0.12 |
| co05 | ~~ | un11 | 5.59 | -0.07 | -0.07 | -0.12 | -0.12 |
| ut09 | ~~ | ut11 | 5.58 | 0.10 | 0.10 | 0.11 | 0.11 |
| fa08 | ~~ | de09 | 5.57 | 0.13 | 0.13 | 0.11 | 0.11 |
| co01 | ~~ | fa05 | 5.57 | 0.06 | 0.06 | 0.14 | 0.14 |
| co06 | ~~ | co10 | 5.54 | 0.09 | 0.09 | 0.12 | 0.12 |
| ut07 | ~~ | fa08 | 5.53 | -0.11 | -0.11 | -0.11 | -0.11 |
| de02 | ~~ | un07 | 5.52 | 0.08 | 0.08 | 0.11 | 0.11 |
| pr05 | ~~ | un07 | 5.46 | 0.09 | 0.09 | 0.11 | 0.11 |
| ut11 | ~~ | de10 | 5.45 | 0.10 | 0.10 | 0.11 | 0.11 |
| ut04 | ~~ | un06 | 5.42 | -0.11 | -0.11 | -0.11 | -0.11 |
| co08 | ~~ | fa08 | 5.41 | -0.11 | -0.11 | -0.11 | -0.11 |
| co04 | ~~ | fa02 | 5.41 | -0.12 | -0.12 | -0.11 | -0.11 |
| pr05 | ~~ | un05 | 5.41 | -0.07 | -0.07 | -0.11 | -0.11 |
| co06 | ~~ | fa10 | 5.39 | -0.09 | -0.09 | -0.11 | -0.11 |
| pr09 | ~~ | co04 | 5.38 | 0.08 | 0.08 | 0.11 | 0.11 |
| ut12 | ~~ | de01 | 5.38 | 0.08 | 0.08 | 0.11 | 0.11 |
| co08 | ~~ | un06 | 5.26 | -0.11 | -0.11 | -0.11 | -0.11 |
| co01 | ~~ | ut04 | 5.22 | -0.08 | -0.08 | -0.11 | -0.11 |
| ut01 | ~~ | un03 | 5.22 | 0.07 | 0.07 | 0.11 | 0.11 |
| pr09 | ~~ | de08 | 5.21 | 0.07 | 0.07 | 0.11 | 0.11 |
| co09 | ~~ | ut04 | 5.19 | -0.08 | -0.08 | -0.11 | -0.11 |
| pr05 | ~~ | pr08 | 5.17 | -0.07 | -0.07 | -0.11 | -0.11 |
| co08 | ~~ | co10 | 5.15 | 0.09 | 0.09 | 0.11 | 0.11 |
| ut05 | ~~ | un07 | 5.15 | 0.08 | 0.08 | 0.11 | 0.11 |
| pr02 | ~~ | pr05 | 5.15 | 0.08 | 0.08 | 0.11 | 0.11 |
| co04 | ~~ | fa10 | 5.12 | 0.10 | 0.10 | 0.11 | 0.11 |
| co01 | ~~ | de01 | 5.09 | 0.07 | 0.07 | 0.11 | 0.11 |
| ut08 | ~~ | de10 | 5.06 | -0.07 | -0.07 | -0.11 | -0.11 |
| pr05 | ~~ | ut07 | 5.05 | -0.10 | -0.10 | -0.11 | -0.11 |
| co08 | ~~ | fa05 | 5.05 | 0.07 | 0.07 | 0.12 | 0.12 |
| pr07 | ~~ | ut01 | 5.04 | -0.06 | -0.06 | -0.11 | -0.11 |
| pr10 | ~~ | co06 | 5.04 | -0.08 | -0.08 | -0.11 | -0.11 |
| co03 | ~~ | un01 | 5.02 | 0.07 | 0.07 | 0.11 | 0.11 |
| co03 | ~~ | ut04 | 5.02 | 0.09 | 0.09 | 0.10 | 0.10 |
| ut03 | ~~ | un04 | 4.99 | -0.08 | -0.08 | -0.11 | -0.11 |
| ut12 | ~~ | fa06 | 4.97 | 0.08 | 0.08 | 0.11 | 0.11 |
| ut05 | ~~ | fa09 | 4.94 | -0.10 | -0.10 | -0.11 | -0.11 |
| pr02 | ~~ | de01 | 4.94 | -0.07 | -0.07 | -0.11 | -0.11 |
| ut04 | ~~ | de08 | 4.92 | 0.08 | 0.08 | 0.11 | 0.11 |
| co10 | ~~ | fa09 | 4.90 | -0.10 | -0.10 | -0.10 | -0.10 |
| ut01 | ~~ | fa05 | 4.89 | 0.05 | 0.05 | 0.13 | 0.13 |
| pr05 | ~~ | fa04 | 4.88 | 0.10 | 0.10 | 0.10 | 0.10 |
| pr08 | ~~ | de02 | 4.86 | -0.06 | -0.06 | -0.11 | -0.11 |
| un07 | ~~ | un12 | 4.86 | -0.07 | -0.07 | -0.11 | -0.11 |
| fa09 | ~~ | un11 | 4.86 | -0.08 | -0.08 | -0.11 | -0.11 |
| fa06 | ~~ | un07 | 4.83 | 0.08 | 0.08 | 0.11 | 0.11 |
| co04 | ~~ | ut02 | 4.82 | 0.07 | 0.07 | 0.11 | 0.11 |
| co04 | ~~ | un09 | 4.80 | -0.09 | -0.09 | -0.10 | -0.10 |
| co03 | ~~ | de09 | 4.80 | 0.10 | 0.10 | 0.10 | 0.10 |
| pr05 | ~~ | co09 | 4.79 | 0.08 | 0.08 | 0.11 | 0.11 |
| ut07 | ~~ | fa10 | 4.76 | 0.09 | 0.09 | 0.10 | 0.10 |
| ut03 | ~~ | de10 | 4.74 | -0.08 | -0.08 | -0.10 | -0.10 |
| fa06 | ~~ | un03 | 4.71 | 0.09 | 0.09 | 0.10 | 0.10 |
| fa10 | ~~ | un10 | 4.70 | -0.08 | -0.08 | -0.10 | -0.10 |
| fa04 | ~~ | un05 | 4.65 | -0.07 | -0.07 | -0.10 | -0.10 |
| fa05 | ~~ | de10 | 4.63 | 0.06 | 0.06 | 0.12 | 0.12 |
| fa04 | ~~ | de06 | 4.63 | 0.10 | 0.10 | 0.10 | 0.10 |
| fa08 | ~~ | un04 | 4.62 | -0.08 | -0.08 | -0.10 | -0.10 |
| co02 | ~~ | ut05 | 4.60 | 0.07 | 0.07 | 0.11 | 0.11 |
| fa05 | ~~ | de03 | 4.58 | 0.07 | 0.07 | 0.12 | 0.12 |
| ut02 | ~~ | ut08 | 4.58 | -0.05 | -0.05 | -0.12 | -0.12 |
| ut04 | ~~ | fa08 | 4.57 | 0.10 | 0.10 | 0.10 | 0.10 |
| co03 | ~~ | co06 | 4.55 | -0.08 | -0.08 | -0.10 | -0.10 |
| pr02 | ~~ | co06 | 4.54 | 0.07 | 0.07 | 0.10 | 0.10 |
| de01 | ~~ | de03 | 4.54 | 0.09 | 0.09 | 0.10 | 0.10 |
| co09 | ~~ | un05 | 4.52 | -0.05 | -0.05 | -0.11 | -0.11 |
| pr10 | ~~ | co02 | 4.51 | 0.07 | 0.07 | 0.10 | 0.10 |
| de05 | ~~ | un03 | 4.50 | 0.10 | 0.10 | 0.10 | 0.10 |
| fa10 | ~~ | de05 | 4.49 | 0.09 | 0.09 | 0.10 | 0.10 |
| pr09 | ~~ | un01 | 4.47 | 0.06 | 0.06 | 0.10 | 0.10 |
| co03 | ~~ | fa01 | 4.45 | 0.06 | 0.06 | 0.11 | 0.11 |
| un03 | ~~ | un08 | 4.43 | -0.08 | -0.08 | -0.10 | -0.10 |
| co05 | ~~ | un01 | 4.40 | -0.05 | -0.05 | -0.11 | -0.11 |
| co03 | ~~ | de02 | 4.39 | -0.07 | -0.07 | -0.10 | -0.10 |
| pr08 | ~~ | un01 | 4.37 | 0.05 | 0.05 | 0.10 | 0.10 |
| pr08 | ~~ | co04 | 4.37 | 0.06 | 0.06 | 0.10 | 0.10 |
| de02 | ~~ | un10 | 4.37 | 0.07 | 0.07 | 0.10 | 0.10 |
| fa01 | ~~ | fa08 | 4.36 | -0.07 | -0.07 | -0.12 | -0.12 |
| pr09 | ~~ | ut06 | 4.34 | -0.06 | -0.06 | -0.10 | -0.10 |
| pr01 | ~~ | co09 | 4.34 | -0.05 | -0.05 | -0.11 | -0.11 |
| co05 | ~~ | de01 | 4.31 | 0.07 | 0.07 | 0.11 | 0.11 |
| ut07 | ~~ | de01 | 4.29 | 0.08 | 0.08 | 0.10 | 0.10 |
| pr06 | ~~ | fa01 | 4.29 | -0.06 | -0.06 | -0.11 | -0.11 |
| ut04 | ~~ | de07 | 4.28 | 0.08 | 0.08 | 0.10 | 0.10 |
| de10 | ~~ | un10 | 4.28 | 0.07 | 0.07 | 0.10 | 0.10 |
| de01 | ~~ | de05 | 4.27 | -0.09 | -0.09 | -0.10 | -0.10 |
| ut02 | ~~ | de03 | 4.25 | -0.06 | -0.06 | -0.11 | -0.11 |
| de01 | ~~ | un08 | 4.23 | 0.07 | 0.07 | 0.10 | 0.10 |
| pr05 | ~~ | un06 | 4.19 | 0.10 | 0.10 | 0.10 | 0.10 |
| co06 | ~~ | co09 | 4.19 | 0.07 | 0.07 | 0.11 | 0.11 |
| pr08 | ~~ | ut11 | 4.18 | -0.06 | -0.06 | -0.10 | -0.10 |
| de06 | ~~ | un07 | 4.16 | 0.08 | 0.08 | 0.10 | 0.10 |
| pr10 | ~~ | fa02 | 4.15 | -0.09 | -0.09 | -0.09 | -0.09 |
| un06 | ~~ | un08 | 4.14 | -0.08 | -0.08 | -0.10 | -0.10 |
| de05 | ~~ | un02 | 4.12 | -0.06 | -0.06 | -0.10 | -0.10 |
| ut06 | ~~ | un05 | 4.12 | 0.05 | 0.05 | 0.10 | 0.10 |
| de02 | ~~ | de07 | 4.06 | -0.07 | -0.07 | -0.10 | -0.10 |
| fa02 | ~~ | de06 | 4.05 | 0.10 | 0.10 | 0.09 | 0.09 |
| co05 | ~~ | un07 | 4.04 | 0.06 | 0.06 | 0.10 | 0.10 |
| ut06 | ~~ | de08 | 4.03 | -0.06 | -0.06 | -0.10 | -0.10 |
| pr07 | ~~ | ut07 | 4.03 | 0.07 | 0.07 | 0.10 | 0.10 |
| ut02 | ~~ | fa06 | 4.02 | -0.05 | -0.05 | -0.10 | -0.10 |
| co10 | ~~ | un01 | 3.97 | -0.06 | -0.06 | -0.10 | -0.10 |
| de02 | ~~ | de10 | 3.96 | 0.07 | 0.07 | 0.10 | 0.10 |
| de01 | ~~ | de02 | 3.96 | 0.07 | 0.07 | 0.10 | 0.10 |
| fa04 | ~~ | de07 | 3.95 | -0.08 | -0.08 | -0.09 | -0.09 |
| pr02 | ~~ | co10 | 3.93 | 0.06 | 0.06 | 0.09 | 0.09 |
| co06 | ~~ | fa08 | 3.93 | -0.09 | -0.09 | -0.09 | -0.09 |
| de10 | ~~ | un03 | 3.90 | -0.08 | -0.08 | -0.10 | -0.10 |
| ut09 | ~~ | fa05 | 3.90 | -0.06 | -0.06 | -0.11 | -0.11 |
| de02 | ~~ | de09 | 3.90 | -0.09 | -0.09 | -0.09 | -0.09 |
| pr06 | ~~ | co05 | 3.90 | -0.06 | -0.06 | -0.10 | -0.10 |
| pr08 | ~~ | co08 | 3.88 | -0.06 | -0.06 | -0.09 | -0.09 |
| pr07 | ~~ | de10 | 3.87 | 0.06 | 0.06 | 0.10 | 0.10 |
| de06 | ~~ | un05 | 3.86 | -0.06 | -0.06 | -0.10 | -0.10 |
| un04 | ~~ | un08 | 3.85 | -0.06 | -0.06 | -0.10 | -0.10 |
| pr02 | ~~ | ut04 | 3.85 | 0.07 | 0.07 | 0.09 | 0.09 |
| de02 | ~~ | un12 | 3.85 | -0.06 | -0.06 | -0.10 | -0.10 |
| ut05 | ~~ | de01 | 3.82 | 0.08 | 0.08 | 0.09 | 0.09 |
| co04 | ~~ | un04 | 3.81 | -0.07 | -0.07 | -0.09 | -0.09 |
| fa09 | ~~ | un02 | 3.77 | 0.06 | 0.06 | 0.10 | 0.10 |
| de09 | ~~ | un12 | 3.76 | 0.08 | 0.08 | 0.09 | 0.09 |
| ut03 | ~~ | ut07 | 3.76 | -0.08 | -0.08 | -0.09 | -0.09 |
| co03 | ~~ | co09 | 3.76 | -0.06 | -0.06 | -0.10 | -0.10 |
| co02 | ~~ | ut02 | 3.75 | -0.05 | -0.05 | -0.10 | -0.10 |
| de05 | ~~ | un01 | 3.74 | 0.06 | 0.06 | 0.09 | 0.09 |
| fa09 | ~~ | un10 | 3.73 | -0.08 | -0.08 | -0.09 | -0.09 |
| co06 | ~~ | fa02 | 3.73 | -0.09 | -0.09 | -0.09 | -0.09 |
| pr01 | ~~ | un11 | 3.72 | -0.05 | -0.05 | -0.10 | -0.10 |
| de07 | ~~ | un06 | 3.72 | -0.08 | -0.08 | -0.09 | -0.09 |
| ut02 | ~~ | de09 | 3.71 | 0.06 | 0.06 | 0.10 | 0.10 |
| de01 | ~~ | un02 | 3.71 | 0.05 | 0.05 | 0.10 | 0.10 |
| ut05 | ~~ | un11 | 3.69 | 0.07 | 0.07 | 0.09 | 0.09 |
| de03 | ~~ | de07 | 3.68 | -0.07 | -0.07 | -0.09 | -0.09 |
| co09 | ~~ | de09 | 3.67 | -0.08 | -0.08 | -0.09 | -0.09 |
| fa02 | ~~ | de10 | 3.66 | 0.08 | 0.08 | 0.09 | 0.09 |
| ut02 | ~~ | de07 | 3.65 | -0.05 | -0.05 | -0.10 | -0.10 |
| un03 | ~~ | un05 | 3.62 | 0.06 | 0.06 | 0.09 | 0.09 |
| ut09 | ~~ | de03 | 3.60 | -0.08 | -0.08 | -0.09 | -0.09 |
| co02 | ~~ | fa01 | 3.59 | 0.05 | 0.05 | 0.11 | 0.11 |
| pr10 | ~~ | co10 | 3.59 | 0.07 | 0.07 | 0.09 | 0.09 |
| de01 | ~~ | de08 | 3.59 | 0.06 | 0.06 | 0.09 | 0.09 |
| ut04 | ~~ | un11 | 3.59 | -0.07 | -0.07 | -0.09 | -0.09 |
| co04 | ~~ | ut05 | 3.55 | -0.08 | -0.08 | -0.09 | -0.09 |
| un06 | ~~ | un10 | 3.53 | 0.08 | 0.08 | 0.09 | 0.09 |
| ut07 | ~~ | ut11 | 3.53 | -0.09 | -0.09 | -0.09 | -0.09 |
| fa02 | ~~ | un08 | 3.53 | 0.07 | 0.07 | 0.09 | 0.09 |
| un06 | ~~ | un12 | 3.52 | -0.07 | -0.07 | -0.09 | -0.09 |
| un01 | ~~ | un12 | 3.52 | 0.05 | 0.05 | 0.09 | 0.09 |
| de05 | ~~ | de06 | 3.50 | -0.08 | -0.08 | -0.09 | -0.09 |
| co06 | ~~ | un03 | 3.48 | 0.08 | 0.08 | 0.09 | 0.09 |
| co06 | ~~ | fa01 | 3.47 | -0.05 | -0.05 | -0.10 | -0.10 |
| pr01 | ~~ | ut12 | 3.45 | 0.05 | 0.05 | 0.09 | 0.09 |
| ut08 | ~~ | de07 | 3.43 | -0.06 | -0.06 | -0.09 | -0.09 |
| fa01 | ~~ | fa10 | 3.42 | 0.07 | 0.07 | 0.12 | 0.12 |
| co01 | ~~ | de03 | 3.42 | 0.06 | 0.06 | 0.09 | 0.09 |
| un02 | ~~ | un08 | 3.41 | -0.05 | -0.05 | -0.10 | -0.10 |
| ut03 | ~~ | un09 | 3.41 | -0.07 | -0.07 | -0.09 | -0.09 |
| pr05 | ~~ | fa05 | 3.41 | -0.06 | -0.06 | -0.10 | -0.10 |
| pr02 | ~~ | fa04 | 3.41 | 0.07 | 0.07 | 0.09 | 0.09 |
| fa06 | ~~ | un01 | 3.39 | -0.05 | -0.05 | -0.09 | -0.09 |
| ut01 | ~~ | de08 | 3.38 | -0.05 | -0.05 | -0.09 | -0.09 |
| pr05 | ~~ | un10 | 3.38 | 0.07 | 0.07 | 0.09 | 0.09 |
| pr09 | ~~ | un08 | 3.36 | 0.05 | 0.05 | 0.09 | 0.09 |
| ut01 | ~~ | fa08 | 3.36 | -0.06 | -0.06 | -0.09 | -0.09 |
| ut01 | ~~ | ut04 | 3.36 | 0.06 | 0.06 | 0.09 | 0.09 |
| co10 | ~~ | un08 | 3.36 | 0.06 | 0.06 | 0.09 | 0.09 |
| fa02 | ~~ | de09 | 3.36 | 0.10 | 0.10 | 0.08 | 0.08 |
| ut03 | ~~ | de08 | 3.35 | 0.06 | 0.06 | 0.09 | 0.09 |
| pr08 | ~~ | pr10 | 3.34 | 0.05 | 0.05 | 0.09 | 0.09 |
| un04 | ~~ | un06 | 3.33 | 0.07 | 0.07 | 0.09 | 0.09 |
| co09 | ~~ | de01 | 3.31 | -0.06 | -0.06 | -0.09 | -0.09 |
| pr09 | ~~ | de02 | 3.31 | 0.06 | 0.06 | 0.09 | 0.09 |
| co01 | ~~ | ut08 | 3.27 | 0.05 | 0.05 | 0.09 | 0.09 |
| pr07 | ~~ | un12 | 3.26 | -0.05 | -0.05 | -0.09 | -0.09 |
| ut12 | ~~ | de07 | 3.25 | -0.06 | -0.06 | -0.09 | -0.09 |
| co03 | ~~ | fa05 | 3.24 | -0.05 | -0.05 | -0.10 | -0.10 |
| co06 | ~~ | fa06 | 3.23 | 0.07 | 0.07 | 0.09 | 0.09 |
| fa05 | ~~ | un10 | 3.21 | 0.05 | 0.05 | 0.10 | 0.10 |
| un07 | ~~ | un08 | 3.20 | -0.06 | -0.06 | -0.09 | -0.09 |
| ut01 | ~~ | un12 | 3.19 | 0.05 | 0.05 | 0.09 | 0.09 |
| fa04 | ~~ | fa05 | 3.18 | 0.06 | 0.06 | 0.10 | 0.10 |
| ut01 | ~~ | de06 | 3.17 | -0.06 | -0.06 | -0.09 | -0.09 |
| ut03 | ~~ | un05 | 3.17 | 0.05 | 0.05 | 0.09 | 0.09 |
| pr09 | ~~ | co08 | 3.14 | -0.06 | -0.06 | -0.08 | -0.08 |
| pr09 | ~~ | fa08 | 3.14 | 0.07 | 0.07 | 0.08 | 0.08 |
| pr02 | ~~ | co02 | 3.13 | 0.05 | 0.05 | 0.09 | 0.09 |
| pr05 | ~~ | pr09 | 3.12 | -0.06 | -0.06 | -0.08 | -0.08 |
| ut03 | ~~ | ut08 | 3.11 | -0.07 | -0.07 | -0.08 | -0.08 |
| pr07 | ~~ | un01 | 3.11 | -0.05 | -0.05 | -0.09 | -0.09 |
| ut01 | ~~ | de01 | 3.10 | -0.05 | -0.05 | -0.09 | -0.09 |
| ut06 | ~~ | fa02 | 3.08 | -0.07 | -0.07 | -0.08 | -0.08 |
| de09 | ~~ | un11 | 3.05 | -0.07 | -0.07 | -0.08 | -0.08 |
| ut09 | ~~ | de07 | 3.04 | 0.06 | 0.06 | 0.08 | 0.08 |
| de02 | ~~ | un05 | 3.00 | -0.05 | -0.05 | -0.09 | -0.09 |
| fa08 | ~~ | de05 | 3.00 | -0.08 | -0.08 | -0.08 | -0.08 |
| pr06 | ~~ | de08 | 3.00 | -0.06 | -0.06 | -0.08 | -0.08 |
| pr07 | ~~ | de05 | 2.99 | -0.06 | -0.06 | -0.08 | -0.08 |
| ut07 | ~~ | un07 | 2.99 | 0.06 | 0.06 | 0.08 | 0.08 |
| fa06 | ~~ | un09 | 2.98 | 0.06 | 0.06 | 0.08 | 0.08 |
| fa05 | ~~ | un06 | 2.98 | -0.06 | -0.06 | -0.09 | -0.09 |
| ut12 | ~~ | un09 | 2.97 | -0.06 | -0.06 | -0.08 | -0.08 |
| ut04 | ~~ | fa04 | 2.97 | -0.08 | -0.08 | -0.08 | -0.08 |
| ut08 | ~~ | de02 | 2.97 | 0.06 | 0.06 | 0.08 | 0.08 |
| co02 | ~~ | un10 | 2.96 | 0.05 | 0.05 | 0.08 | 0.08 |
| pr01 | ~~ | de05 | 2.96 | 0.05 | 0.05 | 0.09 | 0.09 |
| ut04 | ~~ | un08 | 2.95 | 0.06 | 0.06 | 0.08 | 0.08 |
| ut09 | ~~ | fa04 | 2.93 | 0.07 | 0.07 | 0.08 | 0.08 |
| ut03 | ~~ | de03 | 2.93 | -0.07 | -0.07 | -0.08 | -0.08 |
| co09 | ~~ | de10 | 2.92 | 0.05 | 0.05 | 0.09 | 0.09 |
| de03 | ~~ | un11 | 2.92 | -0.06 | -0.06 | -0.08 | -0.08 |
| ut08 | ~~ | un08 | 2.92 | 0.05 | 0.05 | 0.08 | 0.08 |
| ut12 | ~~ | un03 | 2.92 | -0.07 | -0.07 | -0.08 | -0.08 |
| ut05 | ~~ | ut08 | 2.91 | -0.06 | -0.06 | -0.08 | -0.08 |
| pr05 | ~~ | co01 | 2.91 | -0.06 | -0.06 | -0.08 | -0.08 |
| fa05 | ~~ | de05 | 2.91 | -0.05 | -0.05 | -0.09 | -0.09 |
| de07 | ~~ | un09 | 2.91 | 0.06 | 0.06 | 0.08 | 0.08 |
| pr09 | ~~ | ut05 | 2.88 | -0.06 | -0.06 | -0.08 | -0.08 |
| ut07 | ~~ | un06 | 2.88 | -0.08 | -0.08 | -0.08 | -0.08 |
| un04 | ~~ | un12 | 2.87 | 0.05 | 0.05 | 0.08 | 0.08 |
| pr05 | ~~ | fa09 | 2.87 | 0.08 | 0.08 | 0.08 | 0.08 |
| pr08 | ~~ | un08 | 2.86 | 0.04 | 0.04 | 0.08 | 0.08 |
| ut08 | ~~ | fa06 | 2.83 | 0.06 | 0.06 | 0.08 | 0.08 |
| fa10 | ~~ | un06 | 2.82 | -0.08 | -0.08 | -0.08 | -0.08 |
| pr02 | ~~ | fa02 | 2.82 | -0.07 | -0.07 | -0.08 | -0.08 |
| pr01 | ~~ | un09 | 2.82 | -0.05 | -0.05 | -0.08 | -0.08 |
| ut04 | ~~ | ut05 | 2.82 | -0.07 | -0.07 | -0.08 | -0.08 |
| co09 | ~~ | un01 | 2.81 | -0.04 | -0.04 | -0.08 | -0.08 |
| ut06 | ~~ | un08 | 2.81 | -0.05 | -0.05 | -0.08 | -0.08 |
| fa08 | ~~ | de06 | 2.81 | 0.08 | 0.08 | 0.08 | 0.08 |
| de02 | ~~ | un11 | 2.81 | 0.05 | 0.05 | 0.08 | 0.08 |
| ut12 | ~~ | un01 | 2.81 | 0.05 | 0.05 | 0.08 | 0.08 |
| pr05 | ~~ | fa02 | 2.80 | 0.08 | 0.08 | 0.08 | 0.08 |
| pr10 | ~~ | un09 | 2.80 | -0.06 | -0.06 | -0.08 | -0.08 |
| pr01 | ~~ | co05 | 2.80 | 0.04 | 0.04 | 0.09 | 0.09 |
| ut11 | ~~ | un01 | 2.79 | -0.06 | -0.06 | -0.08 | -0.08 |
| ut07 | ~~ | un09 | 2.79 | -0.07 | -0.07 | -0.08 | -0.08 |
| pr07 | ~~ | pr09 | 2.78 | -0.05 | -0.05 | -0.08 | -0.08 |
| fa05 | ~~ | un05 | 2.77 | 0.04 | 0.04 | 0.10 | 0.10 |
| co02 | ~~ | un09 | 2.77 | -0.06 | -0.06 | -0.08 | -0.08 |
| de02 | ~~ | un09 | 2.76 | 0.06 | 0.06 | 0.08 | 0.08 |
| fa01 | ~~ | de07 | 2.75 | 0.04 | 0.04 | 0.09 | 0.09 |
| co10 | ~~ | ut01 | 2.75 | 0.05 | 0.05 | 0.08 | 0.08 |
| fa10 | ~~ | un09 | 2.74 | -0.07 | -0.07 | -0.08 | -0.08 |
| co10 | ~~ | de10 | 2.74 | -0.06 | -0.06 | -0.08 | -0.08 |
| co02 | ~~ | un12 | 2.74 | -0.05 | -0.05 | -0.08 | -0.08 |
| ut03 | ~~ | un12 | 2.74 | 0.06 | 0.06 | 0.08 | 0.08 |
| un01 | ~~ | un11 | 2.73 | -0.05 | -0.05 | -0.08 | -0.08 |
| pr07 | ~~ | co08 | 2.70 | 0.06 | 0.06 | 0.08 | 0.08 |
| co05 | ~~ | co10 | 2.70 | -0.05 | -0.05 | -0.09 | -0.09 |
| pr07 | ~~ | co09 | 2.69 | 0.05 | 0.05 | 0.08 | 0.08 |
| co10 | ~~ | fa02 | 2.68 | -0.07 | -0.07 | -0.08 | -0.08 |
| ut11 | ~~ | fa10 | 2.68 | -0.08 | -0.08 | -0.08 | -0.08 |
| un09 | ~~ | un11 | 2.68 | 0.06 | 0.06 | 0.08 | 0.08 |
| pr02 | ~~ | co05 | 2.68 | -0.05 | -0.05 | -0.08 | -0.08 |
| fa05 | ~~ | de01 | 2.67 | 0.05 | 0.05 | 0.09 | 0.09 |
| un02 | ~~ | un07 | 2.67 | 0.05 | 0.05 | 0.08 | 0.08 |
| co06 | ~~ | ut12 | 2.67 | -0.06 | -0.06 | -0.08 | -0.08 |
| ut02 | ~~ | un01 | 2.67 | 0.03 | 0.03 | 0.09 | 0.09 |
| fa09 | ~~ | de06 | 2.67 | 0.08 | 0.08 | 0.08 | 0.08 |
| de02 | ~~ | de03 | 2.66 | 0.06 | 0.06 | 0.08 | 0.08 |
| de08 | ~~ | un01 | 2.66 | 0.04 | 0.04 | 0.08 | 0.08 |
| co03 | ~~ | un06 | 2.66 | -0.07 | -0.07 | -0.08 | -0.08 |
| co03 | ~~ | ut09 | 2.66 | 0.06 | 0.06 | 0.08 | 0.08 |
| fa02 | ~~ | un09 | 2.65 | -0.07 | -0.07 | -0.08 | -0.08 |
| co09 | ~~ | de07 | 2.63 | -0.05 | -0.05 | -0.08 | -0.08 |
| ut02 | ~~ | fa02 | 2.63 | 0.05 | 0.05 | 0.08 | 0.08 |
| co03 | ~~ | un12 | 2.62 | 0.05 | 0.05 | 0.08 | 0.08 |
| de10 | ~~ | un07 | 2.59 | 0.06 | 0.06 | 0.08 | 0.08 |
| ut08 | ~~ | fa05 | 2.59 | -0.04 | -0.04 | -0.09 | -0.09 |
| ut01 | ~~ | de10 | 2.59 | -0.05 | -0.05 | -0.08 | -0.08 |
| co03 | ~~ | co10 | 2.58 | -0.06 | -0.06 | -0.08 | -0.08 |
| pr07 | ~~ | ut02 | 2.58 | -0.04 | -0.04 | -0.09 | -0.09 |
| pr02 | ~~ | pr08 | 2.57 | -0.04 | -0.04 | -0.08 | -0.08 |
| co05 | ~~ | ut06 | 2.57 | 0.04 | 0.04 | 0.08 | 0.08 |
| co09 | ~~ | co10 | 2.56 | -0.05 | -0.05 | -0.08 | -0.08 |
| pr01 | ~~ | fa08 | 2.55 | -0.05 | -0.05 | -0.08 | -0.08 |
| un11 | ~~ | un12 | 2.55 | 0.05 | 0.05 | 0.08 | 0.08 |
| co08 | ~~ | un12 | 2.54 | -0.06 | -0.06 | -0.08 | -0.08 |
| pr06 | ~~ | de10 | 2.53 | -0.06 | -0.06 | -0.08 | -0.08 |
| co09 | ~~ | un08 | 2.53 | -0.04 | -0.04 | -0.08 | -0.08 |
| un08 | ~~ | un12 | 2.52 | 0.05 | 0.05 | 0.08 | 0.08 |
| ut02 | ~~ | de01 | 2.51 | -0.04 | -0.04 | -0.08 | -0.08 |
| fa04 | ~~ | un02 | 2.50 | 0.05 | 0.05 | 0.08 | 0.08 |
| pr02 | ~~ | ut09 | 2.50 | -0.05 | -0.05 | -0.08 | -0.08 |
| fa09 | ~~ | de08 | 2.49 | -0.06 | -0.06 | -0.08 | -0.08 |
| ut02 | ~~ | ut06 | 2.48 | -0.04 | -0.04 | -0.09 | -0.09 |
| pr07 | ~~ | co04 | 2.48 | -0.06 | -0.06 | -0.08 | -0.08 |
| co05 | ~~ | un08 | 2.48 | -0.04 | -0.04 | -0.08 | -0.08 |
| ut12 | ~~ | de06 | 2.47 | -0.06 | -0.06 | -0.08 | -0.08 |
| co10 | ~~ | ut06 | 2.46 | -0.05 | -0.05 | -0.08 | -0.08 |
| ut06 | ~~ | ut07 | 2.46 | 0.05 | 0.05 | 0.08 | 0.08 |
| co02 | ~~ | un07 | 2.45 | 0.05 | 0.05 | 0.08 | 0.08 |
| co09 | ~~ | ut12 | 2.45 | -0.05 | -0.05 | -0.08 | -0.08 |
| co04 | ~~ | un03 | 2.45 | 0.07 | 0.07 | 0.07 | 0.07 |
| fa01 | ~~ | de01 | 2.44 | -0.05 | -0.05 | -0.08 | -0.08 |
| co03 | ~~ | ut12 | 2.43 | -0.05 | -0.05 | -0.07 | -0.07 |
| pr10 | ~~ | ut07 | 2.43 | 0.06 | 0.06 | 0.07 | 0.07 |
| co05 | ~~ | de10 | 2.42 | 0.05 | 0.05 | 0.08 | 0.08 |
| ut02 | ~~ | de02 | 2.42 | -0.04 | -0.04 | -0.08 | -0.08 |
| fa09 | ~~ | un01 | 2.42 | -0.05 | -0.05 | -0.07 | -0.07 |
| pr06 | ~~ | fa05 | 2.41 | 0.04 | 0.04 | 0.09 | 0.09 |
| fa10 | ~~ | un05 | 2.41 | 0.05 | 0.05 | 0.08 | 0.08 |
| un03 | ~~ | un11 | 2.41 | -0.06 | -0.06 | -0.08 | -0.08 |
| fa05 | ~~ | un08 | 2.40 | -0.04 | -0.04 | -0.09 | -0.09 |
| co08 | ~~ | ut07 | 2.40 | 0.07 | 0.07 | 0.07 | 0.07 |
| un01 | ~~ | un09 | 2.39 | -0.05 | -0.05 | -0.08 | -0.08 |
| fa01 | ~~ | de10 | 2.38 | -0.04 | -0.04 | -0.09 | -0.09 |
| fa02 | ~~ | de05 | 2.38 | -0.08 | -0.08 | -0.07 | -0.07 |
| co01 | ~~ | ut11 | 2.38 | -0.06 | -0.06 | -0.08 | -0.08 |
| pr10 | ~~ | fa06 | 2.38 | 0.06 | 0.06 | 0.07 | 0.07 |
| co02 | ~~ | co06 | 2.38 | 0.05 | 0.05 | 0.08 | 0.08 |
| fa08 | ~~ | un08 | 2.37 | 0.06 | 0.06 | 0.07 | 0.07 |
| ut02 | ~~ | un06 | 2.36 | 0.05 | 0.05 | 0.08 | 0.08 |
| de09 | ~~ | un03 | 2.36 | 0.08 | 0.08 | 0.07 | 0.07 |
| un02 | ~~ | un11 | 2.35 | -0.04 | -0.04 | -0.08 | -0.08 |
| fa02 | ~~ | de02 | 2.33 | -0.06 | -0.06 | -0.07 | -0.07 |
| pr10 | ~~ | ut03 | 2.33 | -0.06 | -0.06 | -0.07 | -0.07 |
| co01 | ~~ | de06 | 2.32 | -0.05 | -0.05 | -0.08 | -0.08 |
| co01 | ~~ | un11 | 2.32 | 0.04 | 0.04 | 0.08 | 0.08 |
| pr06 | ~~ | un03 | 2.29 | 0.06 | 0.06 | 0.07 | 0.07 |
| pr09 | ~~ | un09 | 2.28 | -0.05 | -0.05 | -0.07 | -0.07 |
| co10 | ~~ | de08 | 2.27 | -0.05 | -0.05 | -0.07 | -0.07 |
| co08 | ~~ | de02 | 2.26 | 0.06 | 0.06 | 0.07 | 0.07 |
| co04 | ~~ | un02 | 2.26 | -0.05 | -0.05 | -0.07 | -0.07 |
| ut08 | ~~ | un12 | 2.26 | -0.05 | -0.05 | -0.07 | -0.07 |
| un05 | ~~ | un08 | 2.25 | 0.04 | 0.04 | 0.08 | 0.08 |
| fa01 | ~~ | un08 | 2.25 | 0.04 | 0.04 | 0.08 | 0.08 |
| ut01 | ~~ | ut07 | 2.24 | -0.05 | -0.05 | -0.08 | -0.08 |
| ut06 | ~~ | ut08 | 2.23 | 0.04 | 0.04 | 0.07 | 0.07 |
| co09 | ~~ | un11 | 2.22 | 0.04 | 0.04 | 0.08 | 0.08 |
| pr01 | ~~ | de01 | 2.21 | 0.04 | 0.04 | 0.07 | 0.07 |
| pr10 | ~~ | ut01 | 2.21 | 0.04 | 0.04 | 0.07 | 0.07 |
| pr06 | ~~ | un09 | 2.21 | 0.05 | 0.05 | 0.07 | 0.07 |
| pr05 | ~~ | ut05 | 2.20 | 0.06 | 0.06 | 0.07 | 0.07 |
| pr10 | ~~ | un10 | 2.20 | -0.05 | -0.05 | -0.07 | -0.07 |
| ut07 | ~~ | de02 | 2.19 | 0.05 | 0.05 | 0.07 | 0.07 |
| fa10 | ~~ | un03 | 2.19 | -0.07 | -0.07 | -0.07 | -0.07 |
| de06 | ~~ | de09 | 2.18 | -0.08 | -0.08 | -0.07 | -0.07 |
| pr01 | ~~ | de02 | 2.18 | -0.04 | -0.04 | -0.08 | -0.08 |
| ut12 | ~~ | fa02 | 2.18 | -0.06 | -0.06 | -0.07 | -0.07 |
| ut07 | ~~ | de08 | 2.16 | 0.05 | 0.05 | 0.07 | 0.07 |
| pr09 | ~~ | un04 | 2.15 | -0.04 | -0.04 | -0.07 | -0.07 |
| pr07 | ~~ | un03 | 2.15 | -0.05 | -0.05 | -0.07 | -0.07 |
| pr01 | ~~ | un10 | 2.14 | -0.04 | -0.04 | -0.07 | -0.07 |
| pr05 | ~~ | fa10 | 2.14 | -0.07 | -0.07 | -0.07 | -0.07 |
| co01 | ~~ | fa04 | 2.12 | 0.05 | 0.05 | 0.07 | 0.07 |
| un03 | ~~ | un07 | 2.12 | -0.06 | -0.06 | -0.07 | -0.07 |
| fa01 | ~~ | un06 | 2.12 | -0.05 | -0.05 | -0.08 | -0.08 |
| pr09 | ~~ | ut02 | 2.11 | -0.03 | -0.03 | -0.07 | -0.07 |
| co06 | ~~ | un08 | 2.10 | -0.05 | -0.05 | -0.07 | -0.07 |
| pr01 | ~~ | ut05 | 2.10 | -0.04 | -0.04 | -0.07 | -0.07 |
| co05 | ~~ | un10 | 2.10 | 0.04 | 0.04 | 0.07 | 0.07 |
| pr06 | ~~ | fa02 | 2.09 | -0.06 | -0.06 | -0.07 | -0.07 |
| ut05 | ~~ | un01 | 2.07 | -0.04 | -0.04 | -0.07 | -0.07 |
| pr06 | ~~ | un12 | 2.07 | 0.05 | 0.05 | 0.07 | 0.07 |
| ut12 | ~~ | fa10 | 2.07 | -0.06 | -0.06 | -0.07 | -0.07 |
| ut01 | ~~ | ut12 | 2.07 | -0.04 | -0.04 | -0.07 | -0.07 |
| fa02 | ~~ | de08 | 2.06 | -0.06 | -0.06 | -0.07 | -0.07 |
| ut07 | ~~ | fa02 | 2.04 | -0.07 | -0.07 | -0.07 | -0.07 |
| pr08 | ~~ | fa05 | 2.04 | -0.03 | -0.03 | -0.08 | -0.08 |
| ut02 | ~~ | ut11 | 2.03 | -0.05 | -0.05 | -0.07 | -0.07 |
| de08 | ~~ | un07 | 2.03 | 0.05 | 0.05 | 0.07 | 0.07 |
| ut04 | ~~ | de10 | 2.03 | 0.06 | 0.06 | 0.07 | 0.07 |
| fa08 | ~~ | de07 | 2.03 | -0.06 | -0.06 | -0.07 | -0.07 |
| ut08 | ~~ | fa02 | 2.02 | -0.06 | -0.06 | -0.07 | -0.07 |
| pr05 | ~~ | co03 | 2.01 | 0.06 | 0.06 | 0.07 | 0.07 |
| pr06 | ~~ | ut01 | 2.01 | 0.04 | 0.04 | 0.07 | 0.07 |
| fa05 | ~~ | fa06 | 2.01 | 0.05 | 0.05 | 0.09 | 0.09 |
| co03 | ~~ | un05 | 2.00 | 0.04 | 0.04 | 0.07 | 0.07 |
| pr01 | ~~ | de07 | 2.00 | -0.03 | -0.03 | -0.07 | -0.07 |
| pr02 | ~~ | un08 | 2.00 | 0.04 | 0.04 | 0.07 | 0.07 |
| co02 | ~~ | ut04 | 2.00 | 0.05 | 0.05 | 0.07 | 0.07 |
| pr07 | ~~ | ut06 | 2.00 | -0.04 | -0.04 | -0.07 | -0.07 |
| pr08 | ~~ | fa09 | 1.99 | -0.04 | -0.04 | -0.07 | -0.07 |
| pr07 | ~~ | co02 | 1.99 | -0.04 | -0.04 | -0.07 | -0.07 |
| ut04 | ~~ | de02 | 1.98 | -0.05 | -0.05 | -0.07 | -0.07 |
| pr01 | ~~ | un07 | 1.98 | -0.04 | -0.04 | -0.07 | -0.07 |
| pr08 | ~~ | fa04 | 1.98 | -0.04 | -0.04 | -0.07 | -0.07 |
| ut08 | ~~ | de03 | 1.98 | 0.05 | 0.05 | 0.07 | 0.07 |
| co05 | ~~ | ut02 | 1.97 | -0.03 | -0.03 | -0.08 | -0.08 |
| un01 | ~~ | un03 | 1.96 | -0.05 | -0.05 | -0.07 | -0.07 |
| de06 | ~~ | un02 | 1.96 | 0.04 | 0.04 | 0.07 | 0.07 |
| co06 | ~~ | de09 | 1.95 | 0.07 | 0.07 | 0.07 | 0.07 |
| un01 | ~~ | un06 | 1.94 | -0.05 | -0.05 | -0.07 | -0.07 |
| ut03 | ~~ | de02 | 1.94 | -0.05 | -0.05 | -0.07 | -0.07 |
| de09 | ~~ | un08 | 1.93 | 0.06 | 0.06 | 0.07 | 0.07 |
| de02 | ~~ | un02 | 1.93 | 0.04 | 0.04 | 0.07 | 0.07 |
| ut09 | ~~ | un05 | 1.93 | -0.04 | -0.04 | -0.07 | -0.07 |
| ut05 | ~~ | de09 | 1.92 | -0.07 | -0.07 | -0.06 | -0.06 |
| ut05 | ~~ | ut11 | 1.92 | -0.06 | -0.06 | -0.07 | -0.07 |
| de01 | ~~ | un05 | 1.92 | -0.04 | -0.04 | -0.07 | -0.07 |
| pr01 | ~~ | un02 | 1.91 | -0.03 | -0.03 | -0.07 | -0.07 |
| ut07 | ~~ | fa01 | 1.91 | 0.04 | 0.04 | 0.07 | 0.07 |
| pr08 | ~~ | fa10 | 1.91 | 0.04 | 0.04 | 0.07 | 0.07 |
| ut05 | ~~ | un05 | 1.90 | -0.04 | -0.04 | -0.07 | -0.07 |
| ut01 | ~~ | ut08 | 1.90 | -0.04 | -0.04 | -0.07 | -0.07 |
| co05 | ~~ | ut11 | 1.90 | 0.05 | 0.05 | 0.07 | 0.07 |
| pr01 | ~~ | un12 | 1.89 | 0.03 | 0.03 | 0.07 | 0.07 |
| co08 | ~~ | fa02 | 1.89 | -0.07 | -0.07 | -0.06 | -0.06 |
| de07 | ~~ | un03 | 1.88 | 0.05 | 0.05 | 0.06 | 0.06 |
| pr02 | ~~ | un02 | 1.87 | 0.03 | 0.03 | 0.07 | 0.07 |
| fa09 | ~~ | un08 | 1.86 | 0.05 | 0.05 | 0.07 | 0.07 |
| fa04 | ~~ | un04 | 1.86 | 0.05 | 0.05 | 0.06 | 0.06 |
| fa08 | ~~ | un11 | 1.85 | -0.05 | -0.05 | -0.06 | -0.06 |
| ut02 | ~~ | de10 | 1.84 | -0.03 | -0.03 | -0.07 | -0.07 |
| pr10 | ~~ | fa01 | 1.82 | -0.04 | -0.04 | -0.07 | -0.07 |
| ut02 | ~~ | fa09 | 1.82 | 0.04 | 0.04 | 0.07 | 0.07 |
| pr08 | ~~ | pr09 | 1.80 | 0.03 | 0.03 | 0.07 | 0.07 |
| co05 | ~~ | un02 | 1.80 | 0.03 | 0.03 | 0.07 | 0.07 |
| de03 | ~~ | un09 | 1.79 | -0.05 | -0.05 | -0.06 | -0.06 |
| co05 | ~~ | de07 | 1.79 | -0.04 | -0.04 | -0.07 | -0.07 |
| co10 | ~~ | fa04 | 1.77 | 0.06 | 0.06 | 0.06 | 0.06 |
| ut06 | ~~ | un12 | 1.76 | -0.04 | -0.04 | -0.07 | -0.07 |
| fa08 | ~~ | de01 | 1.76 | -0.06 | -0.06 | -0.06 | -0.06 |
| pr10 | ~~ | un11 | 1.76 | 0.04 | 0.04 | 0.06 | 0.06 |
| co05 | ~~ | de09 | 1.75 | -0.05 | -0.05 | -0.07 | -0.07 |
| co02 | ~~ | co09 | 1.75 | -0.04 | -0.04 | -0.07 | -0.07 |
| fa02 | ~~ | de07 | 1.75 | -0.05 | -0.05 | -0.06 | -0.06 |
| ut04 | ~~ | un09 | 1.75 | 0.05 | 0.05 | 0.06 | 0.06 |
| pr06 | ~~ | co02 | 1.75 | -0.04 | -0.04 | -0.06 | -0.06 |
| ut05 | ~~ | de05 | 1.74 | -0.06 | -0.06 | -0.06 | -0.06 |
| co03 | ~~ | un04 | 1.74 | -0.04 | -0.04 | -0.06 | -0.06 |
| ut01 | ~~ | de07 | 1.74 | 0.03 | 0.03 | 0.07 | 0.07 |
| co02 | ~~ | de02 | 1.73 | 0.04 | 0.04 | 0.07 | 0.07 |
| pr05 | ~~ | fa06 | 1.72 | 0.05 | 0.05 | 0.06 | 0.06 |
| ut04 | ~~ | fa05 | 1.72 | 0.04 | 0.04 | 0.07 | 0.07 |
| de10 | ~~ | un11 | 1.72 | 0.04 | 0.04 | 0.07 | 0.07 |
| de02 | ~~ | un08 | 1.72 | -0.04 | -0.04 | -0.06 | -0.06 |
| co10 | ~~ | de03 | 1.72 | 0.05 | 0.05 | 0.06 | 0.06 |
| pr10 | ~~ | co05 | 1.71 | -0.04 | -0.04 | -0.07 | -0.07 |
| co01 | ~~ | ut01 | 1.71 | -0.03 | -0.03 | -0.07 | -0.07 |
| pr09 | ~~ | co09 | 1.71 | -0.04 | -0.04 | -0.06 | -0.06 |
| co10 | ~~ | ut02 | 1.69 | 0.03 | 0.03 | 0.07 | 0.07 |
| de08 | ~~ | un12 | 1.68 | 0.04 | 0.04 | 0.06 | 0.06 |
| pr05 | ~~ | un11 | 1.68 | 0.05 | 0.05 | 0.06 | 0.06 |
| pr01 | ~~ | de06 | 1.68 | -0.04 | -0.04 | -0.07 | -0.07 |
| co01 | ~~ | de09 | 1.68 | -0.05 | -0.05 | -0.06 | -0.06 |
| ut09 | ~~ | un03 | 1.68 | -0.05 | -0.05 | -0.06 | -0.06 |
| ut07 | ~~ | de09 | 1.67 | -0.06 | -0.06 | -0.06 | -0.06 |
| pr08 | ~~ | ut04 | 1.66 | -0.04 | -0.04 | -0.06 | -0.06 |
| pr08 | ~~ | co10 | 1.65 | -0.03 | -0.03 | -0.06 | -0.06 |
| pr05 | ~~ | un02 | 1.65 | -0.04 | -0.04 | -0.06 | -0.06 |
| ut01 | ~~ | de09 | 1.65 | 0.05 | 0.05 | 0.06 | 0.06 |
| ut09 | ~~ | de06 | 1.64 | -0.05 | -0.05 | -0.06 | -0.06 |
| co04 | ~~ | un11 | 1.63 | -0.05 | -0.05 | -0.06 | -0.06 |
| co06 | ~~ | de07 | 1.63 | 0.04 | 0.04 | 0.06 | 0.06 |
| fa08 | ~~ | un07 | 1.63 | -0.05 | -0.05 | -0.06 | -0.06 |
| ut04 | ~~ | un02 | 1.62 | -0.04 | -0.04 | -0.06 | -0.06 |
| pr10 | ~~ | un02 | 1.62 | 0.04 | 0.04 | 0.06 | 0.06 |
| de06 | ~~ | un04 | 1.62 | 0.04 | 0.04 | 0.06 | 0.06 |
| pr09 | ~~ | ut09 | 1.61 | -0.04 | -0.04 | -0.06 | -0.06 |
| pr08 | ~~ | un11 | 1.61 | 0.03 | 0.03 | 0.06 | 0.06 |
| ut12 | ~~ | de10 | 1.61 | 0.04 | 0.04 | 0.06 | 0.06 |
| co02 | ~~ | de10 | 1.61 | -0.04 | -0.04 | -0.06 | -0.06 |
| ut12 | ~~ | fa01 | 1.61 | -0.04 | -0.04 | -0.07 | -0.07 |
| co05 | ~~ | un09 | 1.61 | -0.04 | -0.04 | -0.07 | -0.07 |
| pr07 | ~~ | pr08 | 1.60 | 0.03 | 0.03 | 0.07 | 0.07 |
| ut03 | ~~ | de06 | 1.59 | -0.05 | -0.05 | -0.06 | -0.06 |
| ut09 | ~~ | un06 | 1.59 | -0.06 | -0.06 | -0.06 | -0.06 |
| de06 | ~~ | de07 | 1.59 | -0.05 | -0.05 | -0.06 | -0.06 |
| ut08 | ~~ | un07 | 1.58 | -0.04 | -0.04 | -0.06 | -0.06 |
| co06 | ~~ | un11 | 1.57 | -0.04 | -0.04 | -0.06 | -0.06 |
| co01 | ~~ | fa06 | 1.57 | 0.04 | 0.04 | 0.06 | 0.06 |
| fa06 | ~~ | un06 | 1.57 | -0.05 | -0.05 | -0.06 | -0.06 |
| ut03 | ~~ | un03 | 1.57 | 0.05 | 0.05 | 0.06 | 0.06 |
| pr07 | ~~ | fa04 | 1.57 | 0.05 | 0.05 | 0.06 | 0.06 |
| ut06 | ~~ | un06 | 1.56 | -0.05 | -0.05 | -0.06 | -0.06 |
| pr09 | ~~ | de07 | 1.56 | -0.04 | -0.04 | -0.06 | -0.06 |
| un04 | ~~ | un11 | 1.53 | -0.04 | -0.04 | -0.06 | -0.06 |
| fa04 | ~~ | de01 | 1.53 | 0.05 | 0.05 | 0.06 | 0.06 |
| co08 | ~~ | de07 | 1.53 | -0.04 | -0.04 | -0.06 | -0.06 |
| de09 | ~~ | un02 | 1.52 | -0.04 | -0.04 | -0.06 | -0.06 |
| de10 | ~~ | un02 | 1.52 | -0.03 | -0.03 | -0.06 | -0.06 |
| ut04 | ~~ | un12 | 1.51 | 0.04 | 0.04 | 0.06 | 0.06 |
| ut06 | ~~ | un03 | 1.51 | 0.04 | 0.04 | 0.06 | 0.06 |
| ut04 | ~~ | ut08 | 1.50 | -0.05 | -0.05 | -0.06 | -0.06 |
| co09 | ~~ | fa01 | 1.50 | -0.03 | -0.03 | -0.07 | -0.07 |
| ut06 | ~~ | de05 | 1.50 | 0.04 | 0.04 | 0.06 | 0.06 |
| pr02 | ~~ | de03 | 1.50 | 0.04 | 0.04 | 0.06 | 0.06 |
| pr05 | ~~ | de09 | 1.49 | -0.06 | -0.06 | -0.06 | -0.06 |
| ut06 | ~~ | fa08 | 1.49 | -0.04 | -0.04 | -0.06 | -0.06 |
| ut05 | ~~ | de03 | 1.49 | -0.05 | -0.05 | -0.06 | -0.06 |
| pr09 | ~~ | de05 | 1.48 | 0.04 | 0.04 | 0.06 | 0.06 |
| ut03 | ~~ | un11 | 1.48 | -0.04 | -0.04 | -0.06 | -0.06 |
| pr01 | ~~ | co02 | 1.46 | 0.03 | 0.03 | 0.06 | 0.06 |
| fa06 | ~~ | de10 | 1.46 | 0.04 | 0.04 | 0.06 | 0.06 |
| pr02 | ~~ | un09 | 1.46 | -0.04 | -0.04 | -0.06 | -0.06 |
| pr08 | ~~ | un06 | 1.45 | -0.04 | -0.04 | -0.06 | -0.06 |
| ut01 | ~~ | ut06 | 1.45 | -0.03 | -0.03 | -0.06 | -0.06 |
| fa05 | ~~ | un04 | 1.44 | -0.03 | -0.03 | -0.07 | -0.07 |
| pr10 | ~~ | de05 | 1.44 | 0.05 | 0.05 | 0.06 | 0.06 |
| co09 | ~~ | un09 | 1.44 | 0.04 | 0.04 | 0.06 | 0.06 |
| un03 | ~~ | un12 | 1.43 | -0.04 | -0.04 | -0.06 | -0.06 |
| co05 | ~~ | un03 | 1.41 | -0.04 | -0.04 | -0.06 | -0.06 |
| fa02 | ~~ | un04 | 1.40 | -0.04 | -0.04 | -0.06 | -0.06 |
| ut03 | ~~ | ut11 | 1.39 | -0.06 | -0.06 | -0.05 | -0.05 |
| fa06 | ~~ | un02 | 1.39 | 0.03 | 0.03 | 0.06 | 0.06 |
| fa09 | ~~ | un12 | 1.39 | 0.04 | 0.04 | 0.06 | 0.06 |
| co10 | ~~ | ut08 | 1.39 | -0.04 | -0.04 | -0.06 | -0.06 |
| pr07 | ~~ | co03 | 1.39 | 0.04 | 0.04 | 0.06 | 0.06 |
| ut11 | ~~ | un07 | 1.39 | 0.05 | 0.05 | 0.06 | 0.06 |
| co01 | ~~ | de07 | 1.38 | -0.03 | -0.03 | -0.06 | -0.06 |
| ut06 | ~~ | de01 | 1.38 | -0.04 | -0.04 | -0.06 | -0.06 |
| pr06 | ~~ | de09 | 1.38 | -0.05 | -0.05 | -0.05 | -0.05 |
| un09 | ~~ | un10 | 1.37 | 0.04 | 0.04 | 0.06 | 0.06 |
| ut03 | ~~ | ut09 | 1.37 | -0.05 | -0.05 | -0.06 | -0.06 |
| pr10 | ~~ | fa08 | 1.37 | -0.05 | -0.05 | -0.05 | -0.05 |
| co01 | ~~ | un02 | 1.37 | -0.03 | -0.03 | -0.06 | -0.06 |
| co06 | ~~ | ut01 | 1.37 | 0.03 | 0.03 | 0.06 | 0.06 |
| ut03 | ~~ | un01 | 1.36 | 0.04 | 0.04 | 0.06 | 0.06 |
| co05 | ~~ | fa09 | 1.36 | 0.04 | 0.04 | 0.06 | 0.06 |
| fa10 | ~~ | un12 | 1.35 | 0.04 | 0.04 | 0.06 | 0.06 |
| ut06 | ~~ | ut09 | 1.34 | 0.04 | 0.04 | 0.06 | 0.06 |
| de06 | ~~ | un03 | 1.34 | -0.05 | -0.05 | -0.05 | -0.05 |
| de03 | ~~ | un07 | 1.34 | 0.04 | 0.04 | 0.06 | 0.06 |
| ut04 | ~~ | un04 | 1.34 | 0.04 | 0.04 | 0.05 | 0.05 |
| fa01 | ~~ | un11 | 1.33 | 0.03 | 0.03 | 0.06 | 0.06 |
| co05 | ~~ | ut04 | 1.33 | -0.04 | -0.04 | -0.06 | -0.06 |
| co09 | ~~ | de08 | 1.33 | -0.03 | -0.03 | -0.06 | -0.06 |
| un07 | ~~ | un11 | 1.33 | 0.04 | 0.04 | 0.06 | 0.06 |
| ut04 | ~~ | fa06 | 1.32 | -0.05 | -0.05 | -0.05 | -0.05 |
| ut07 | ~~ | de10 | 1.32 | -0.04 | -0.04 | -0.06 | -0.06 |
| de02 | ~~ | un04 | 1.31 | -0.03 | -0.03 | -0.06 | -0.06 |
| co02 | ~~ | co08 | 1.31 | -0.04 | -0.04 | -0.06 | -0.06 |
| un05 | ~~ | un06 | 1.30 | -0.04 | -0.04 | -0.06 | -0.06 |
| co04 | ~~ | de01 | 1.30 | -0.05 | -0.05 | -0.05 | -0.05 |
| pr08 | ~~ | ut03 | 1.30 | 0.03 | 0.03 | 0.05 | 0.05 |
| co03 | ~~ | un02 | 1.29 | -0.03 | -0.03 | -0.06 | -0.06 |
| ut08 | ~~ | fa04 | 1.28 | 0.04 | 0.04 | 0.05 | 0.05 |
| pr08 | ~~ | un12 | 1.28 | 0.03 | 0.03 | 0.06 | 0.06 |
| de09 | ~~ | un04 | 1.27 | -0.04 | -0.04 | -0.05 | -0.05 |
| ut02 | ~~ | un11 | 1.27 | -0.03 | -0.03 | -0.06 | -0.06 |
| ut05 | ~~ | un04 | 1.27 | -0.04 | -0.04 | -0.05 | -0.05 |
| pr07 | ~~ | de01 | 1.25 | -0.04 | -0.04 | -0.05 | -0.05 |
| co09 | ~~ | ut05 | 1.25 | 0.04 | 0.04 | 0.06 | 0.06 |
| pr07 | ~~ | un11 | 1.25 | 0.03 | 0.03 | 0.06 | 0.06 |
| fa09 | ~~ | un04 | 1.24 | 0.04 | 0.04 | 0.05 | 0.05 |
| co02 | ~~ | co05 | 1.23 | -0.03 | -0.03 | -0.06 | -0.06 |
| co02 | ~~ | co10 | 1.23 | 0.04 | 0.04 | 0.06 | 0.06 |
| ut04 | ~~ | de03 | 1.23 | 0.05 | 0.05 | 0.05 | 0.05 |
| pr07 | ~~ | ut08 | 1.23 | 0.03 | 0.03 | 0.05 | 0.05 |
| ut11 | ~~ | un12 | 1.22 | -0.04 | -0.04 | -0.05 | -0.05 |
| pr09 | ~~ | co10 | 1.22 | -0.04 | -0.04 | -0.05 | -0.05 |
| de07 | ~~ | un01 | 1.22 | 0.03 | 0.03 | 0.05 | 0.05 |
| de07 | ~~ | un12 | 1.22 | 0.03 | 0.03 | 0.05 | 0.05 |
| co03 | ~~ | fa06 | 1.22 | 0.04 | 0.04 | 0.05 | 0.05 |
| co01 | ~~ | ut07 | 1.21 | -0.04 | -0.04 | -0.05 | -0.05 |
| ut09 | ~~ | fa08 | 1.21 | -0.05 | -0.05 | -0.05 | -0.05 |
| un03 | ~~ | un10 | 1.21 | 0.04 | 0.04 | 0.05 | 0.05 |
| fa09 | ~~ | de03 | 1.21 | -0.05 | -0.05 | -0.05 | -0.05 |
| co04 | ~~ | de06 | 1.20 | -0.05 | -0.05 | -0.05 | -0.05 |
| co04 | ~~ | de03 | 1.20 | -0.05 | -0.05 | -0.05 | -0.05 |
| ut05 | ~~ | fa06 | 1.20 | 0.04 | 0.04 | 0.05 | 0.05 |
| co04 | ~~ | ut11 | 1.20 | -0.05 | -0.05 | -0.05 | -0.05 |
| pr01 | ~~ | co10 | 1.20 | 0.03 | 0.03 | 0.06 | 0.06 |
| pr07 | ~~ | un10 | 1.20 | 0.03 | 0.03 | 0.05 | 0.05 |
| ut07 | ~~ | fa05 | 1.19 | -0.03 | -0.03 | -0.06 | -0.06 |
| ut02 | ~~ | fa01 | 1.18 | 0.02 | 0.02 | 0.06 | 0.06 |
| co04 | ~~ | fa09 | 1.18 | -0.05 | -0.05 | -0.05 | -0.05 |
| co06 | ~~ | ut08 | 1.18 | 0.04 | 0.04 | 0.05 | 0.05 |
| ut01 | ~~ | fa09 | 1.17 | -0.04 | -0.04 | -0.05 | -0.05 |
| fa05 | ~~ | fa10 | 1.15 | -0.04 | -0.04 | -0.07 | -0.07 |
| co01 | ~~ | un09 | 1.15 | 0.03 | 0.03 | 0.05 | 0.05 |
| co02 | ~~ | de08 | 1.14 | -0.03 | -0.03 | -0.05 | -0.05 |
| co03 | ~~ | de10 | 1.14 | -0.04 | -0.04 | -0.05 | -0.05 |
| co02 | ~~ | fa06 | 1.14 | 0.03 | 0.03 | 0.05 | 0.05 |
| pr06 | ~~ | ut08 | 1.14 | -0.04 | -0.04 | -0.05 | -0.05 |
| pr08 | ~~ | co09 | 1.14 | -0.03 | -0.03 | -0.05 | -0.05 |
| fa08 | ~~ | un02 | 1.14 | -0.03 | -0.03 | -0.05 | -0.05 |
| pr02 | ~~ | un07 | 1.14 | -0.03 | -0.03 | -0.05 | -0.05 |
| fa05 | ~~ | un03 | 1.14 | 0.03 | 0.03 | 0.06 | 0.06 |
| pr05 | ~~ | pr06 | 1.13 | -0.04 | -0.04 | -0.05 | -0.05 |
| fa10 | ~~ | de09 | 1.13 | 0.05 | 0.05 | 0.05 | 0.05 |
| co02 | ~~ | ut03 | 1.12 | -0.04 | -0.04 | -0.05 | -0.05 |
| co05 | ~~ | un12 | 1.12 | -0.03 | -0.03 | -0.05 | -0.05 |
| ut07 | ~~ | un03 | 1.12 | -0.04 | -0.04 | -0.05 | -0.05 |
| pr02 | ~~ | pr07 | 1.11 | -0.03 | -0.03 | -0.05 | -0.05 |
| pr06 | ~~ | de06 | 1.11 | -0.04 | -0.04 | -0.05 | -0.05 |
| ut08 | ~~ | un09 | 1.10 | -0.04 | -0.04 | -0.05 | -0.05 |
| ut09 | ~~ | un02 | 1.10 | -0.03 | -0.03 | -0.05 | -0.05 |
| de07 | ~~ | de09 | 1.09 | -0.04 | -0.04 | -0.05 | -0.05 |
| ut11 | ~~ | un04 | 1.09 | 0.04 | 0.04 | 0.05 | 0.05 |
| ut09 | ~~ | fa02 | 1.09 | 0.05 | 0.05 | 0.05 | 0.05 |
| ut05 | ~~ | de08 | 1.09 | -0.04 | -0.04 | -0.05 | -0.05 |
| co01 | ~~ | un08 | 1.09 | -0.03 | -0.03 | -0.05 | -0.05 |
| co09 | ~~ | fa02 | 1.09 | 0.04 | 0.04 | 0.05 | 0.05 |
| pr05 | ~~ | un03 | 1.08 | -0.05 | -0.05 | -0.05 | -0.05 |
| ut07 | ~~ | un04 | 1.07 | 0.03 | 0.03 | 0.05 | 0.05 |
| pr07 | ~~ | un02 | 1.07 | 0.03 | 0.03 | 0.05 | 0.05 |
| ut08 | ~~ | de06 | 1.07 | -0.04 | -0.04 | -0.05 | -0.05 |
| pr06 | ~~ | un05 | 1.07 | 0.03 | 0.03 | 0.05 | 0.05 |
| pr09 | ~~ | fa01 | 1.07 | 0.03 | 0.03 | 0.06 | 0.06 |
| pr10 | ~~ | de10 | 1.06 | -0.04 | -0.04 | -0.05 | -0.05 |
| fa08 | ~~ | un09 | 1.06 | -0.05 | -0.05 | -0.05 | -0.05 |
| pr07 | ~~ | un04 | 1.05 | -0.03 | -0.03 | -0.05 | -0.05 |
| fa01 | ~~ | de03 | 1.05 | -0.03 | -0.03 | -0.06 | -0.06 |
| pr07 | ~~ | fa02 | 1.04 | -0.04 | -0.04 | -0.05 | -0.05 |
| pr01 | ~~ | co03 | 1.04 | -0.03 | -0.03 | -0.05 | -0.05 |
| co01 | ~~ | co05 | 1.04 | 0.03 | 0.03 | 0.06 | 0.06 |
| co10 | ~~ | un02 | 1.04 | 0.03 | 0.03 | 0.05 | 0.05 |
| pr02 | ~~ | fa10 | 1.04 | -0.04 | -0.04 | -0.05 | -0.05 |
| fa09 | ~~ | un09 | 1.03 | -0.04 | -0.04 | -0.05 | -0.05 |
| co10 | ~~ | un12 | 1.02 | 0.03 | 0.03 | 0.05 | 0.05 |
| ut05 | ~~ | fa10 | 1.02 | 0.04 | 0.04 | 0.05 | 0.05 |
| pr08 | ~~ | un09 | 1.01 | -0.03 | -0.03 | -0.05 | -0.05 |
| fa02 | ~~ | un01 | 1.00 | -0.04 | -0.04 | -0.05 | -0.05 |
| pr09 | ~~ | un10 | 1.00 | -0.03 | -0.03 | -0.05 | -0.05 |
| co01 | ~~ | un12 | 1.00 | -0.03 | -0.03 | -0.05 | -0.05 |
| co05 | ~~ | co06 | 0.97 | 0.03 | 0.03 | 0.05 | 0.05 |
| co04 | ~~ | fa01 | 0.97 | 0.03 | 0.03 | 0.05 | 0.05 |
| pr06 | ~~ | co10 | 0.97 | 0.04 | 0.04 | 0.05 | 0.05 |
| ut09 | ~~ | un04 | 0.96 | -0.03 | -0.03 | -0.05 | -0.05 |
| pr06 | ~~ | un04 | 0.96 | 0.03 | 0.03 | 0.05 | 0.05 |
| co04 | ~~ | co10 | 0.96 | 0.04 | 0.04 | 0.05 | 0.05 |
| co01 | ~~ | un04 | 0.96 | -0.03 | -0.03 | -0.05 | -0.05 |
| co08 | ~~ | fa01 | 0.96 | -0.03 | -0.03 | -0.05 | -0.05 |
| pr10 | ~~ | un03 | 0.95 | 0.04 | 0.04 | 0.05 | 0.05 |
| ut08 | ~~ | un02 | 0.94 | 0.03 | 0.03 | 0.05 | 0.05 |
| un05 | ~~ | un11 | 0.94 | -0.03 | -0.03 | -0.05 | -0.05 |
| pr02 | ~~ | un10 | 0.94 | 0.03 | 0.03 | 0.05 | 0.05 |
| pr07 | ~~ | de03 | 0.94 | 0.03 | 0.03 | 0.05 | 0.05 |
| ut09 | ~~ | ut12 | 0.93 | 0.03 | 0.03 | 0.05 | 0.05 |
| co06 | ~~ | un05 | 0.93 | -0.03 | -0.03 | -0.05 | -0.05 |
| pr01 | ~~ | de09 | 0.93 | 0.03 | 0.03 | 0.05 | 0.05 |
| de08 | ~~ | un11 | 0.92 | 0.03 | 0.03 | 0.05 | 0.05 |
| ut11 | ~~ | un05 | 0.91 | -0.03 | -0.03 | -0.05 | -0.05 |
| de01 | ~~ | un07 | 0.91 | 0.04 | 0.04 | 0.05 | 0.05 |
| co01 | ~~ | fa09 | 0.91 | -0.04 | -0.04 | -0.05 | -0.05 |
| ut12 | ~~ | un04 | 0.91 | 0.03 | 0.03 | 0.05 | 0.05 |
| co01 | ~~ | fa02 | 0.91 | 0.04 | 0.04 | 0.05 | 0.05 |
| ut06 | ~~ | un07 | 0.90 | 0.03 | 0.03 | 0.05 | 0.05 |
| de08 | ~~ | de10 | 0.89 | 0.03 | 0.03 | 0.05 | 0.05 |
| de08 | ~~ | un09 | 0.89 | 0.03 | 0.03 | 0.05 | 0.05 |
| co02 | ~~ | co04 | 0.88 | 0.04 | 0.04 | 0.05 | 0.05 |
| ut09 | ~~ | de08 | 0.87 | 0.03 | 0.03 | 0.05 | 0.05 |
| ut06 | ~~ | fa04 | 0.87 | 0.03 | 0.03 | 0.04 | 0.04 |
| co03 | ~~ | de01 | 0.86 | -0.03 | -0.03 | -0.04 | -0.04 |
| pr02 | ~~ | fa05 | 0.86 | -0.02 | -0.02 | -0.05 | -0.05 |
| pr10 | ~~ | un12 | 0.86 | -0.03 | -0.03 | -0.04 | -0.04 |
| pr10 | ~~ | fa05 | 0.86 | -0.03 | -0.03 | -0.05 | -0.05 |
| co10 | ~~ | fa06 | 0.86 | 0.03 | 0.03 | 0.04 | 0.04 |
| ut05 | ~~ | ut12 | 0.85 | 0.03 | 0.03 | 0.05 | 0.05 |
| pr10 | ~~ | ut09 | 0.85 | -0.03 | -0.03 | -0.04 | -0.04 |
| pr10 | ~~ | ut06 | 0.85 | 0.03 | 0.03 | 0.04 | 0.04 |
| pr10 | ~~ | de01 | 0.85 | 0.04 | 0.04 | 0.04 | 0.04 |
| ut02 | ~~ | ut07 | 0.85 | -0.03 | -0.03 | -0.05 | -0.05 |
| ut11 | ~~ | un08 | 0.85 | -0.04 | -0.04 | -0.04 | -0.04 |
| de05 | ~~ | de07 | 0.84 | 0.03 | 0.03 | 0.04 | 0.04 |
| co02 | ~~ | un02 | 0.84 | 0.02 | 0.02 | 0.05 | 0.05 |
| ut08 | ~~ | un01 | 0.83 | 0.03 | 0.03 | 0.04 | 0.04 |
| co09 | ~~ | ut06 | 0.83 | -0.02 | -0.02 | -0.05 | -0.05 |
| co01 | ~~ | un03 | 0.83 | -0.03 | -0.03 | -0.04 | -0.04 |
| ut12 | ~~ | fa09 | 0.83 | -0.04 | -0.04 | -0.04 | -0.04 |
| pr05 | ~~ | un08 | 0.83 | -0.03 | -0.03 | -0.04 | -0.04 |
| de07 | ~~ | un11 | 0.82 | -0.03 | -0.03 | -0.04 | -0.04 |
| pr10 | ~~ | un08 | 0.82 | 0.03 | 0.03 | 0.04 | 0.04 |
| ut01 | ~~ | un10 | 0.82 | -0.02 | -0.02 | -0.05 | -0.05 |
| fa05 | ~~ | de02 | 0.82 | 0.02 | 0.02 | 0.05 | 0.05 |
| de10 | ~~ | un04 | 0.81 | 0.03 | 0.03 | 0.04 | 0.04 |
| co10 | ~~ | de07 | 0.81 | -0.03 | -0.03 | -0.04 | -0.04 |
| de07 | ~~ | un02 | 0.81 | -0.02 | -0.02 | -0.05 | -0.05 |
| un06 | ~~ | un07 | 0.81 | 0.04 | 0.04 | 0.04 | 0.04 |
| pr08 | ~~ | ut05 | 0.81 | -0.03 | -0.03 | -0.04 | -0.04 |
| co02 | ~~ | fa05 | 0.81 | -0.02 | -0.02 | -0.05 | -0.05 |
| ut11 | ~~ | fa02 | 0.80 | -0.05 | -0.05 | -0.04 | -0.04 |
| ut07 | ~~ | de03 | 0.80 | -0.04 | -0.04 | -0.04 | -0.04 |
| un04 | ~~ | un10 | 0.80 | -0.03 | -0.03 | -0.04 | -0.04 |
| co08 | ~~ | de08 | 0.80 | -0.03 | -0.03 | -0.04 | -0.04 |
| co04 | ~~ | ut08 | 0.79 | 0.03 | 0.03 | 0.04 | 0.04 |
| pr09 | ~~ | fa02 | 0.79 | -0.03 | -0.03 | -0.04 | -0.04 |
| pr01 | ~~ | fa02 | 0.79 | -0.03 | -0.03 | -0.04 | -0.04 |
| fa01 | ~~ | de05 | 0.79 | 0.03 | 0.03 | 0.05 | 0.05 |
| de10 | ~~ | un12 | 0.79 | -0.03 | -0.03 | -0.04 | -0.04 |
| pr01 | ~~ | ut09 | 0.79 | 0.02 | 0.02 | 0.04 | 0.04 |
| co06 | ~~ | de10 | 0.78 | -0.03 | -0.03 | -0.04 | -0.04 |
| pr08 | ~~ | fa01 | 0.78 | 0.02 | 0.02 | 0.05 | 0.05 |
| ut04 | ~~ | de09 | 0.77 | -0.04 | -0.04 | -0.04 | -0.04 |
| de01 | ~~ | un04 | 0.77 | -0.03 | -0.03 | -0.04 | -0.04 |
| pr06 | ~~ | un01 | 0.76 | -0.03 | -0.03 | -0.04 | -0.04 |
| fa04 | ~~ | un11 | 0.76 | 0.03 | 0.03 | 0.04 | 0.04 |
| de07 | ~~ | de08 | 0.75 | 0.03 | 0.03 | 0.04 | 0.04 |
| pr01 | ~~ | fa01 | 0.75 | 0.02 | 0.02 | 0.05 | 0.05 |
| co02 | ~~ | ut06 | 0.75 | -0.02 | -0.02 | -0.04 | -0.04 |
| de03 | ~~ | un04 | 0.75 | -0.03 | -0.03 | -0.04 | -0.04 |
| pr10 | ~~ | co04 | 0.74 | 0.04 | 0.04 | 0.04 | 0.04 |
| ut09 | ~~ | un10 | 0.74 | 0.03 | 0.03 | 0.04 | 0.04 |
| ut08 | ~~ | un10 | 0.73 | -0.03 | -0.03 | -0.04 | -0.04 |
| de03 | ~~ | de08 | 0.73 | -0.03 | -0.03 | -0.04 | -0.04 |
| ut11 | ~~ | fa05 | 0.73 | -0.03 | -0.03 | -0.05 | -0.05 |
| ut04 | ~~ | de01 | 0.73 | -0.04 | -0.04 | -0.04 | -0.04 |
| co09 | ~~ | ut01 | 0.73 | -0.02 | -0.02 | -0.04 | -0.04 |
| ut07 | ~~ | de07 | 0.73 | -0.03 | -0.03 | -0.04 | -0.04 |
| pr06 | ~~ | fa08 | 0.72 | -0.04 | -0.04 | -0.04 | -0.04 |
| pr02 | ~~ | un12 | 0.72 | 0.02 | 0.02 | 0.04 | 0.04 |
| pr07 | ~~ | de08 | 0.71 | -0.02 | -0.02 | -0.04 | -0.04 |
| co03 | ~~ | fa10 | 0.71 | 0.03 | 0.03 | 0.04 | 0.04 |
| pr06 | ~~ | de05 | 0.71 | 0.03 | 0.03 | 0.04 | 0.04 |
| pr05 | ~~ | ut06 | 0.70 | -0.03 | -0.03 | -0.04 | -0.04 |
| ut11 | ~~ | de08 | 0.70 | 0.03 | 0.03 | 0.04 | 0.04 |
| un01 | ~~ | un02 | 0.69 | -0.02 | -0.02 | -0.04 | -0.04 |
| pr07 | ~~ | fa10 | 0.69 | 0.03 | 0.03 | 0.04 | 0.04 |
| ut09 | ~~ | fa01 | 0.69 | 0.02 | 0.02 | 0.05 | 0.05 |
| ut05 | ~~ | fa08 | 0.68 | 0.04 | 0.04 | 0.04 | 0.04 |
| ut07 | ~~ | de05 | 0.67 | -0.04 | -0.04 | -0.04 | -0.04 |
| pr08 | ~~ | un05 | 0.67 | 0.02 | 0.02 | 0.04 | 0.04 |
| de02 | ~~ | un06 | 0.67 | 0.03 | 0.03 | 0.04 | 0.04 |
| fa01 | ~~ | un12 | 0.66 | -0.02 | -0.02 | -0.05 | -0.05 |
| co08 | ~~ | fa06 | 0.66 | -0.03 | -0.03 | -0.04 | -0.04 |
| co08 | ~~ | fa10 | 0.66 | -0.04 | -0.04 | -0.04 | -0.04 |
| un05 | ~~ | un10 | 0.65 | 0.02 | 0.02 | 0.04 | 0.04 |
| ut07 | ~~ | un08 | 0.64 | 0.03 | 0.03 | 0.04 | 0.04 |
| fa01 | ~~ | fa02 | 0.64 | -0.03 | -0.03 | -0.05 | -0.05 |
| de06 | ~~ | de08 | 0.64 | -0.03 | -0.03 | -0.04 | -0.04 |
| pr09 | ~~ | fa05 | 0.64 | -0.02 | -0.02 | -0.04 | -0.04 |
| co03 | ~~ | ut02 | 0.63 | 0.02 | 0.02 | 0.04 | 0.04 |
| fa10 | ~~ | de07 | 0.63 | -0.03 | -0.03 | -0.04 | -0.04 |
| ut01 | ~~ | un11 | 0.62 | -0.02 | -0.02 | -0.04 | -0.04 |
| pr08 | ~~ | co06 | 0.62 | -0.02 | -0.02 | -0.04 | -0.04 |
| ut11 | ~~ | un10 | 0.62 | 0.03 | 0.03 | 0.04 | 0.04 |
| ut08 | ~~ | fa08 | 0.62 | -0.03 | -0.03 | -0.04 | -0.04 |
| de08 | ~~ | un08 | 0.62 | -0.02 | -0.02 | -0.04 | -0.04 |
| pr01 | ~~ | fa10 | 0.62 | -0.02 | -0.02 | -0.04 | -0.04 |
| ut11 | ~~ | un06 | 0.62 | -0.04 | -0.04 | -0.04 | -0.04 |
| fa05 | ~~ | un07 | 0.61 | 0.02 | 0.02 | 0.04 | 0.04 |
| de05 | ~~ | un09 | 0.61 | -0.03 | -0.03 | -0.04 | -0.04 |
| de07 | ~~ | un07 | 0.61 | 0.03 | 0.03 | 0.04 | 0.04 |
| co05 | ~~ | ut07 | 0.61 | 0.03 | 0.03 | 0.04 | 0.04 |
| ut08 | ~~ | fa09 | 0.61 | -0.03 | -0.03 | -0.04 | -0.04 |
| pr10 | ~~ | de03 | 0.60 | -0.03 | -0.03 | -0.04 | -0.04 |
| pr09 | ~~ | de09 | 0.60 | 0.03 | 0.03 | 0.04 | 0.04 |
| pr10 | ~~ | co03 | 0.60 | 0.03 | 0.03 | 0.04 | 0.04 |
| fa01 | ~~ | de02 | 0.59 | -0.02 | -0.02 | -0.04 | -0.04 |
| pr09 | ~~ | de06 | 0.59 | -0.03 | -0.03 | -0.04 | -0.04 |
| pr02 | ~~ | ut07 | 0.59 | -0.03 | -0.03 | -0.04 | -0.04 |
| ut12 | ~~ | un10 | 0.58 | 0.02 | 0.02 | 0.04 | 0.04 |
| ut05 | ~~ | un12 | 0.58 | -0.03 | -0.03 | -0.04 | -0.04 |
| ut12 | ~~ | un05 | 0.58 | -0.02 | -0.02 | -0.04 | -0.04 |
| co02 | ~~ | de03 | 0.58 | 0.03 | 0.03 | 0.04 | 0.04 |
| pr01 | ~~ | co06 | 0.58 | -0.02 | -0.02 | -0.04 | -0.04 |
| co08 | ~~ | un08 | 0.57 | -0.03 | -0.03 | -0.04 | -0.04 |
| pr05 | ~~ | pr07 | 0.57 | -0.03 | -0.03 | -0.04 | -0.04 |
| pr05 | ~~ | pr10 | 0.57 | -0.03 | -0.03 | -0.04 | -0.04 |
| co05 | ~~ | un05 | 0.57 | -0.02 | -0.02 | -0.04 | -0.04 |
| co03 | ~~ | fa09 | 0.57 | -0.03 | -0.03 | -0.04 | -0.04 |
| ut08 | ~~ | fa10 | 0.57 | 0.03 | 0.03 | 0.04 | 0.04 |
| ut03 | ~~ | ut05 | 0.57 | -0.03 | -0.03 | -0.04 | -0.04 |
| ut05 | ~~ | un02 | 0.56 | -0.02 | -0.02 | -0.04 | -0.04 |
| fa10 | ~~ | un04 | 0.56 | 0.03 | 0.03 | 0.04 | 0.04 |
| co01 | ~~ | fa10 | 0.56 | -0.03 | -0.03 | -0.04 | -0.04 |
| co05 | ~~ | fa04 | 0.55 | 0.03 | 0.03 | 0.04 | 0.04 |
| ut07 | ~~ | ut12 | 0.55 | 0.03 | 0.03 | 0.04 | 0.04 |
| ut03 | ~~ | ut06 | 0.54 | -0.03 | -0.03 | -0.04 | -0.04 |
| de08 | ~~ | un06 | 0.54 | -0.03 | -0.03 | -0.04 | -0.04 |
| co10 | ~~ | ut12 | 0.54 | 0.03 | 0.03 | 0.04 | 0.04 |
| pr06 | ~~ | ut03 | 0.53 | -0.03 | -0.03 | -0.03 | -0.03 |
| pr05 | ~~ | de02 | 0.53 | 0.03 | 0.03 | 0.04 | 0.04 |
| pr08 | ~~ | un04 | 0.53 | -0.02 | -0.02 | -0.04 | -0.04 |
| co04 | ~~ | ut09 | 0.53 | 0.03 | 0.03 | 0.03 | 0.03 |
| fa05 | ~~ | un11 | 0.52 | -0.02 | -0.02 | -0.04 | -0.04 |
| ut02 | ~~ | un09 | 0.52 | 0.02 | 0.02 | 0.04 | 0.04 |
| fa10 | ~~ | de06 | 0.52 | -0.03 | -0.03 | -0.03 | -0.03 |
| de03 | ~~ | un01 | 0.52 | -0.02 | -0.02 | -0.03 | -0.03 |
| pr09 | ~~ | un03 | 0.52 | -0.03 | -0.03 | -0.03 | -0.03 |
| fa09 | ~~ | de10 | 0.52 | 0.03 | 0.03 | 0.03 | 0.03 |
| pr02 | ~~ | co01 | 0.52 | -0.02 | -0.02 | -0.04 | -0.04 |
| pr10 | ~~ | ut02 | 0.52 | 0.02 | 0.02 | 0.04 | 0.04 |
| pr02 | ~~ | un01 | 0.51 | -0.02 | -0.02 | -0.03 | -0.03 |
| co05 | ~~ | un06 | 0.51 | 0.03 | 0.03 | 0.04 | 0.04 |
| ut11 | ~~ | un11 | 0.51 | 0.03 | 0.03 | 0.03 | 0.03 |
| ut09 | ~~ | un08 | 0.51 | 0.02 | 0.02 | 0.03 | 0.03 |
| pr09 | ~~ | un12 | 0.51 | -0.02 | -0.02 | -0.03 | -0.03 |
| pr01 | ~~ | pr02 | 0.51 | 0.02 | 0.02 | 0.04 | 0.04 |
| un03 | ~~ | un09 | 0.51 | 0.03 | 0.03 | 0.03 | 0.03 |
| co01 | ~~ | ut05 | 0.50 | 0.02 | 0.02 | 0.04 | 0.04 |
| pr01 | ~~ | un06 | 0.50 | -0.02 | -0.02 | -0.03 | -0.03 |
| pr01 | ~~ | pr08 | 0.50 | 0.01 | 0.01 | 0.04 | 0.04 |
| pr09 | ~~ | co05 | 0.50 | -0.02 | -0.02 | -0.04 | -0.04 |
| ut04 | ~~ | un05 | 0.50 | -0.02 | -0.02 | -0.03 | -0.03 |
| pr05 | ~~ | un12 | 0.50 | 0.03 | 0.03 | 0.03 | 0.03 |
| ut12 | ~~ | un11 | 0.50 | 0.02 | 0.02 | 0.03 | 0.03 |
| co04 | ~~ | fa05 | 0.49 | 0.02 | 0.02 | 0.04 | 0.04 |
| ut09 | ~~ | un11 | 0.49 | 0.02 | 0.02 | 0.03 | 0.03 |
| ut09 | ~~ | de09 | 0.49 | -0.03 | -0.03 | -0.03 | -0.03 |
| co03 | ~~ | ut08 | 0.49 | -0.02 | -0.02 | -0.03 | -0.03 |
| co05 | ~~ | de02 | 0.48 | 0.02 | 0.02 | 0.04 | 0.04 |
| pr10 | ~~ | un04 | 0.48 | -0.02 | -0.02 | -0.03 | -0.03 |
| co04 | ~~ | ut12 | 0.48 | -0.03 | -0.03 | -0.03 | -0.03 |
| co10 | ~~ | de06 | 0.47 | 0.03 | 0.03 | 0.03 | 0.03 |
| ut05 | ~~ | fa01 | 0.47 | -0.02 | -0.02 | -0.04 | -0.04 |
| un04 | ~~ | un07 | 0.47 | -0.02 | -0.02 | -0.03 | -0.03 |
| de02 | ~~ | de08 | 0.47 | 0.02 | 0.02 | 0.04 | 0.04 |
| pr06 | ~~ | fa04 | 0.47 | -0.03 | -0.03 | -0.03 | -0.03 |
| pr07 | ~~ | fa08 | 0.46 | 0.03 | 0.03 | 0.03 | 0.03 |
| co04 | ~~ | de08 | 0.46 | 0.03 | 0.03 | 0.03 | 0.03 |
| pr08 | ~~ | fa06 | 0.46 | 0.02 | 0.02 | 0.03 | 0.03 |
| co05 | ~~ | fa01 | 0.45 | -0.02 | -0.02 | -0.04 | -0.04 |
| pr08 | ~~ | de10 | 0.45 | 0.02 | 0.02 | 0.03 | 0.03 |
| pr02 | ~~ | pr06 | 0.45 | 0.02 | 0.02 | 0.03 | 0.03 |
| pr07 | ~~ | ut12 | 0.45 | 0.02 | 0.02 | 0.03 | 0.03 |
| ut07 | ~~ | fa09 | 0.45 | -0.03 | -0.03 | -0.03 | -0.03 |
| fa01 | ~~ | fa04 | 0.44 | 0.02 | 0.02 | 0.04 | 0.04 |
| co04 | ~~ | ut07 | 0.44 | -0.03 | -0.03 | -0.03 | -0.03 |
| ut08 | ~~ | un11 | 0.44 | -0.02 | -0.02 | -0.03 | -0.03 |
| de07 | ~~ | un04 | 0.44 | 0.02 | 0.02 | 0.03 | 0.03 |
| co01 | ~~ | un06 | 0.44 | 0.03 | 0.03 | 0.03 | 0.03 |
| un03 | ~~ | un06 | 0.44 | -0.03 | -0.03 | -0.03 | -0.03 |
| fa04 | ~~ | un03 | 0.43 | 0.03 | 0.03 | 0.03 | 0.03 |
| co03 | ~~ | ut07 | 0.43 | 0.03 | 0.03 | 0.03 | 0.03 |
| ut01 | ~~ | un04 | 0.43 | 0.02 | 0.02 | 0.03 | 0.03 |
| pr10 | ~~ | de08 | 0.42 | -0.02 | -0.02 | -0.03 | -0.03 |
| ut05 | ~~ | de07 | 0.42 | 0.02 | 0.02 | 0.03 | 0.03 |
| de03 | ~~ | un03 | 0.42 | 0.03 | 0.03 | 0.03 | 0.03 |
| co08 | ~~ | un09 | 0.42 | 0.03 | 0.03 | 0.03 | 0.03 |
| pr02 | ~~ | ut02 | 0.42 | -0.02 | -0.02 | -0.03 | -0.03 |
| co10 | ~~ | ut07 | 0.41 | 0.02 | 0.02 | 0.03 | 0.03 |
| ut12 | ~~ | fa08 | 0.41 | 0.03 | 0.03 | 0.03 | 0.03 |
| co03 | ~~ | un07 | 0.41 | -0.02 | -0.02 | -0.03 | -0.03 |
| ut12 | ~~ | un07 | 0.41 | 0.02 | 0.02 | 0.03 | 0.03 |
| co01 | ~~ | un10 | 0.41 | -0.02 | -0.02 | -0.03 | -0.03 |
| co06 | ~~ | ut03 | 0.41 | -0.03 | -0.03 | -0.03 | -0.03 |
| co01 | ~~ | ut03 | 0.40 | 0.02 | 0.02 | 0.03 | 0.03 |
| co06 | ~~ | un02 | 0.40 | 0.02 | 0.02 | 0.03 | 0.03 |
| pr06 | ~~ | co06 | 0.40 | 0.02 | 0.02 | 0.03 | 0.03 |
| pr09 | ~~ | ut01 | 0.40 | -0.02 | -0.02 | -0.03 | -0.03 |
| pr08 | ~~ | ut07 | 0.39 | -0.02 | -0.02 | -0.03 | -0.03 |
| co03 | ~~ | ut06 | 0.39 | 0.02 | 0.02 | 0.03 | 0.03 |
| ut06 | ~~ | fa05 | 0.39 | 0.02 | 0.02 | 0.04 | 0.04 |
| de03 | ~~ | un05 | 0.39 | -0.02 | -0.02 | -0.03 | -0.03 |
| un01 | ~~ | un04 | 0.39 | -0.02 | -0.02 | -0.03 | -0.03 |
| pr02 | ~~ | de09 | 0.39 | 0.03 | 0.03 | 0.03 | 0.03 |
| co02 | ~~ | ut01 | 0.39 | 0.02 | 0.02 | 0.03 | 0.03 |
| co09 | ~~ | un04 | 0.39 | 0.02 | 0.02 | 0.03 | 0.03 |
| ut06 | ~~ | un09 | 0.38 | -0.02 | -0.02 | -0.03 | -0.03 |
| co01 | ~~ | ut09 | 0.38 | 0.02 | 0.02 | 0.03 | 0.03 |
| ut09 | ~~ | fa06 | 0.38 | -0.02 | -0.02 | -0.03 | -0.03 |
| pr01 | ~~ | pr06 | 0.38 | 0.02 | 0.02 | 0.03 | 0.03 |
| un10 | ~~ | un12 | 0.38 | -0.02 | -0.02 | -0.03 | -0.03 |
| de06 | ~~ | un12 | 0.37 | -0.02 | -0.02 | -0.03 | -0.03 |
| pr09 | ~~ | fa10 | 0.37 | -0.02 | -0.02 | -0.03 | -0.03 |
| fa01 | ~~ | fa09 | 0.37 | 0.02 | 0.02 | 0.04 | 0.04 |
| pr08 | ~~ | un03 | 0.37 | 0.02 | 0.02 | 0.03 | 0.03 |
| ut08 | ~~ | de08 | 0.37 | -0.02 | -0.02 | -0.03 | -0.03 |
| pr02 | ~~ | pr09 | 0.37 | -0.02 | -0.02 | -0.03 | -0.03 |
| de07 | ~~ | un05 | 0.37 | 0.02 | 0.02 | 0.03 | 0.03 |
| ut06 | ~~ | de10 | 0.36 | -0.02 | -0.02 | -0.03 | -0.03 |
| co08 | ~~ | ut08 | 0.36 | -0.02 | -0.02 | -0.03 | -0.03 |
| un10 | ~~ | un11 | 0.36 | 0.02 | 0.02 | 0.03 | 0.03 |
| fa05 | ~~ | de07 | 0.36 | -0.02 | -0.02 | -0.03 | -0.03 |
| pr08 | ~~ | co01 | 0.36 | -0.01 | -0.01 | -0.03 | -0.03 |
| ut06 | ~~ | un10 | 0.36 | -0.02 | -0.02 | -0.03 | -0.03 |
| co09 | ~~ | un10 | 0.36 | 0.02 | 0.02 | 0.03 | 0.03 |
| co03 | ~~ | de06 | 0.35 | -0.02 | -0.02 | -0.03 | -0.03 |
| fa10 | ~~ | un11 | 0.35 | -0.02 | -0.02 | -0.03 | -0.03 |
| ut09 | ~~ | un01 | 0.35 | 0.02 | 0.02 | 0.03 | 0.03 |
| fa05 | ~~ | un02 | 0.35 | -0.01 | -0.01 | -0.03 | -0.03 |
| co01 | ~~ | co02 | 0.35 | -0.02 | -0.02 | -0.03 | -0.03 |
| fa06 | ~~ | un12 | 0.35 | -0.02 | -0.02 | -0.03 | -0.03 |
| pr10 | ~~ | co01 | 0.35 | -0.02 | -0.02 | -0.03 | -0.03 |
| ut08 | ~~ | un03 | 0.35 | 0.02 | 0.02 | 0.03 | 0.03 |
| de01 | ~~ | un11 | 0.34 | 0.02 | 0.02 | 0.03 | 0.03 |
| pr01 | ~~ | ut04 | 0.34 | -0.02 | -0.02 | -0.03 | -0.03 |
| fa06 | ~~ | de07 | 0.34 | 0.02 | 0.02 | 0.03 | 0.03 |
| pr10 | ~~ | fa10 | 0.34 | -0.02 | -0.02 | -0.03 | -0.03 |
| fa10 | ~~ | de03 | 0.33 | -0.03 | -0.03 | -0.03 | -0.03 |
| co03 | ~~ | fa08 | 0.33 | 0.02 | 0.02 | 0.03 | 0.03 |
| pr09 | ~~ | un02 | 0.33 | 0.01 | 0.01 | 0.03 | 0.03 |
| un04 | ~~ | un05 | 0.33 | -0.01 | -0.01 | -0.03 | -0.03 |
| de01 | ~~ | un12 | 0.33 | -0.02 | -0.02 | -0.03 | -0.03 |
| ut11 | ~~ | ut12 | 0.32 | 0.02 | 0.02 | 0.03 | 0.03 |
| de05 | ~~ | de08 | 0.32 | -0.02 | -0.02 | -0.03 | -0.03 |
| co10 | ~~ | de05 | 0.32 | 0.02 | 0.02 | 0.03 | 0.03 |
| pr07 | ~~ | ut04 | 0.32 | -0.02 | -0.02 | -0.03 | -0.03 |
| pr02 | ~~ | ut08 | 0.32 | 0.02 | 0.02 | 0.03 | 0.03 |
| de02 | ~~ | de06 | 0.31 | 0.02 | 0.02 | 0.03 | 0.03 |
| pr08 | ~~ | ut01 | 0.31 | 0.01 | 0.01 | 0.03 | 0.03 |
| co02 | ~~ | de06 | 0.31 | -0.02 | -0.02 | -0.03 | -0.03 |
| ut06 | ~~ | ut11 | 0.31 | 0.02 | 0.02 | 0.03 | 0.03 |
| co09 | ~~ | de03 | 0.31 | 0.02 | 0.02 | 0.03 | 0.03 |
| pr09 | ~~ | fa06 | 0.30 | 0.02 | 0.02 | 0.03 | 0.03 |
| co05 | ~~ | fa02 | 0.30 | -0.02 | -0.02 | -0.03 | -0.03 |
| co08 | ~~ | ut01 | 0.30 | -0.02 | -0.02 | -0.03 | -0.03 |
| pr09 | ~~ | un06 | 0.30 | -0.02 | -0.02 | -0.03 | -0.03 |
| co04 | ~~ | fa08 | 0.30 | -0.03 | -0.03 | -0.03 | -0.03 |
| pr08 | ~~ | ut08 | 0.30 | 0.01 | 0.01 | 0.03 | 0.03 |
| de06 | ~~ | un06 | 0.29 | 0.03 | 0.03 | 0.03 | 0.03 |
| de01 | ~~ | un03 | 0.29 | -0.02 | -0.02 | -0.03 | -0.03 |
| co01 | ~~ | ut06 | 0.29 | -0.01 | -0.01 | -0.03 | -0.03 |
| co05 | ~~ | ut09 | 0.29 | -0.02 | -0.02 | -0.03 | -0.03 |
| de07 | ~~ | un08 | 0.29 | -0.02 | -0.02 | -0.03 | -0.03 |
| pr06 | ~~ | ut02 | 0.29 | -0.01 | -0.01 | -0.03 | -0.03 |
| co02 | ~~ | ut11 | 0.28 | -0.02 | -0.02 | -0.03 | -0.03 |
| pr01 | ~~ | ut02 | 0.28 | 0.01 | 0.01 | 0.03 | 0.03 |
| fa01 | ~~ | un09 | 0.28 | -0.02 | -0.02 | -0.03 | -0.03 |
| pr02 | ~~ | ut11 | 0.28 | 0.02 | 0.02 | 0.02 | 0.02 |
| co10 | ~~ | fa05 | 0.28 | 0.02 | 0.02 | 0.03 | 0.03 |
| ut08 | ~~ | de09 | 0.28 | -0.02 | -0.02 | -0.02 | -0.02 |
| pr08 | ~~ | de07 | 0.28 | 0.01 | 0.01 | 0.03 | 0.03 |
| co06 | ~~ | ut05 | 0.28 | 0.02 | 0.02 | 0.03 | 0.03 |
| pr02 | ~~ | fa01 | 0.28 | 0.01 | 0.01 | 0.03 | 0.03 |
| co02 | ~~ | fa04 | 0.27 | 0.02 | 0.02 | 0.02 | 0.02 |
| ut08 | ~~ | ut09 | 0.27 | 0.02 | 0.02 | 0.03 | 0.03 |
| fa06 | ~~ | un05 | 0.27 | -0.01 | -0.01 | -0.03 | -0.03 |
| pr09 | ~~ | fa09 | 0.27 | -0.02 | -0.02 | -0.02 | -0.02 |
| ut06 | ~~ | fa01 | 0.26 | 0.01 | 0.01 | 0.03 | 0.03 |
| pr06 | ~~ | co03 | 0.26 | 0.02 | 0.02 | 0.02 | 0.02 |
| pr08 | ~~ | fa02 | 0.26 | 0.02 | 0.02 | 0.02 | 0.02 |
| pr09 | ~~ | co03 | 0.26 | -0.02 | -0.02 | -0.02 | -0.02 |
| pr05 | ~~ | ut03 | 0.26 | -0.02 | -0.02 | -0.02 | -0.02 |
| pr09 | ~~ | co06 | 0.26 | -0.02 | -0.02 | -0.02 | -0.02 |
| de01 | ~~ | un10 | 0.26 | -0.02 | -0.02 | -0.02 | -0.02 |
| co01 | ~~ | de08 | 0.26 | 0.01 | 0.01 | 0.03 | 0.03 |
| ut06 | ~~ | un02 | 0.26 | 0.01 | 0.01 | 0.03 | 0.03 |
| pr06 | ~~ | fa09 | 0.26 | -0.02 | -0.02 | -0.02 | -0.02 |
| ut04 | ~~ | fa02 | 0.26 | 0.02 | 0.02 | 0.02 | 0.02 |
| fa08 | ~~ | un12 | 0.26 | 0.02 | 0.02 | 0.02 | 0.02 |
| pr02 | ~~ | de07 | 0.26 | -0.01 | -0.01 | -0.02 | -0.02 |
| de10 | ~~ | un05 | 0.26 | -0.01 | -0.01 | -0.03 | -0.03 |
| fa01 | ~~ | un05 | 0.25 | -0.01 | -0.01 | -0.03 | -0.03 |
| pr06 | ~~ | fa10 | 0.25 | -0.02 | -0.02 | -0.02 | -0.02 |
| un03 | ~~ | un04 | 0.25 | 0.02 | 0.02 | 0.02 | 0.02 |
| ut11 | ~~ | un03 | 0.25 | -0.02 | -0.02 | -0.02 | -0.02 |
| pr07 | ~~ | fa05 | 0.25 | 0.01 | 0.01 | 0.03 | 0.03 |
| ut08 | ~~ | fa01 | 0.25 | 0.01 | 0.01 | 0.03 | 0.03 |
| co05 | ~~ | ut05 | 0.25 | -0.02 | -0.02 | -0.03 | -0.03 |
| ut04 | ~~ | ut12 | 0.25 | -0.02 | -0.02 | -0.02 | -0.02 |
| pr09 | ~~ | co02 | 0.24 | 0.01 | 0.01 | 0.02 | 0.02 |
| co04 | ~~ | co06 | 0.24 | -0.02 | -0.02 | -0.02 | -0.02 |
| pr02 | ~~ | fa08 | 0.24 | 0.02 | 0.02 | 0.02 | 0.02 |
| ut07 | ~~ | un05 | 0.24 | -0.01 | -0.01 | -0.02 | -0.02 |
| co08 | ~~ | un03 | 0.24 | -0.02 | -0.02 | -0.02 | -0.02 |
| pr02 | ~~ | co09 | 0.24 | -0.01 | -0.01 | -0.02 | -0.02 |
| co10 | ~~ | fa01 | 0.24 | -0.01 | -0.01 | -0.03 | -0.03 |
| pr06 | ~~ | un02 | 0.24 | -0.01 | -0.01 | -0.02 | -0.02 |
| pr02 | ~~ | de08 | 0.24 | -0.01 | -0.01 | -0.02 | -0.02 |
| co05 | ~~ | fa10 | 0.24 | 0.02 | 0.02 | 0.03 | 0.03 |
| ut11 | ~~ | de07 | 0.23 | -0.02 | -0.02 | -0.02 | -0.02 |
| co10 | ~~ | fa10 | 0.23 | -0.02 | -0.02 | -0.02 | -0.02 |
| de01 | ~~ | un01 | 0.23 | -0.01 | -0.01 | -0.02 | -0.02 |
| co08 | ~~ | ut06 | 0.22 | 0.02 | 0.02 | 0.02 | 0.02 |
| ut01 | ~~ | fa01 | 0.22 | -0.01 | -0.01 | -0.03 | -0.03 |
| pr06 | ~~ | pr08 | 0.22 | 0.01 | 0.01 | 0.02 | 0.02 |
| co01 | ~~ | co09 | 0.22 | -0.01 | -0.01 | -0.03 | -0.03 |
| ut09 | ~~ | de10 | 0.22 | 0.02 | 0.02 | 0.02 | 0.02 |
| pr08 | ~~ | co05 | 0.22 | 0.01 | 0.01 | 0.02 | 0.02 |
| de09 | ~~ | un01 | 0.22 | 0.02 | 0.02 | 0.02 | 0.02 |
| pr10 | ~~ | fa04 | 0.21 | 0.02 | 0.02 | 0.02 | 0.02 |
| pr09 | ~~ | ut03 | 0.21 | 0.02 | 0.02 | 0.02 | 0.02 |
| fa02 | ~~ | un07 | 0.21 | -0.02 | -0.02 | -0.02 | -0.02 |
| pr07 | ~~ | co05 | 0.21 | 0.01 | 0.01 | 0.02 | 0.02 |
| ut02 | ~~ | de08 | 0.21 | 0.01 | 0.01 | 0.02 | 0.02 |
| ut05 | ~~ | de06 | 0.21 | -0.02 | -0.02 | -0.02 | -0.02 |
| ut11 | ~~ | de03 | 0.21 | 0.02 | 0.02 | 0.02 | 0.02 |
| pr02 | ~~ | un03 | 0.21 | -0.02 | -0.02 | -0.02 | -0.02 |
| pr07 | ~~ | de09 | 0.21 | -0.02 | -0.02 | -0.02 | -0.02 |
| un02 | ~~ | un09 | 0.21 | 0.01 | 0.01 | 0.02 | 0.02 |
| ut05 | ~~ | ut06 | 0.21 | -0.02 | -0.02 | -0.02 | -0.02 |
| pr05 | ~~ | un04 | 0.21 | 0.02 | 0.02 | 0.02 | 0.02 |
| de06 | ~~ | un11 | 0.21 | 0.02 | 0.02 | 0.02 | 0.02 |
| co03 | ~~ | un03 | 0.21 | 0.02 | 0.02 | 0.02 | 0.02 |
| pr10 | ~~ | de06 | 0.20 | -0.02 | -0.02 | -0.02 | -0.02 |
| co09 | ~~ | ut02 | 0.20 | 0.01 | 0.01 | 0.02 | 0.02 |
| fa02 | ~~ | un02 | 0.20 | 0.02 | 0.02 | 0.02 | 0.02 |
| co04 | ~~ | ut06 | 0.20 | 0.02 | 0.02 | 0.02 | 0.02 |
| ut03 | ~~ | fa01 | 0.20 | -0.01 | -0.01 | -0.02 | -0.02 |
| co08 | ~~ | de06 | 0.20 | -0.02 | -0.02 | -0.02 | -0.02 |
| ut04 | ~~ | un10 | 0.20 | -0.02 | -0.02 | -0.02 | -0.02 |
| ut11 | ~~ | fa08 | 0.19 | -0.02 | -0.02 | -0.02 | -0.02 |
| fa02 | ~~ | un11 | 0.19 | 0.02 | 0.02 | 0.02 | 0.02 |
| pr05 | ~~ | fa01 | 0.19 | 0.01 | 0.01 | 0.02 | 0.02 |
| pr07 | ~~ | un05 | 0.18 | -0.01 | -0.01 | -0.02 | -0.02 |
| fa01 | ~~ | fa06 | 0.18 | -0.01 | -0.01 | -0.03 | -0.03 |
| co08 | ~~ | de01 | 0.18 | 0.02 | 0.02 | 0.02 | 0.02 |
| pr09 | ~~ | ut04 | 0.18 | -0.02 | -0.02 | -0.02 | -0.02 |
| ut06 | ~~ | un11 | 0.18 | -0.01 | -0.01 | -0.02 | -0.02 |
| pr01 | ~~ | un08 | 0.18 | -0.01 | -0.01 | -0.02 | -0.02 |
| co02 | ~~ | ut07 | 0.18 | 0.01 | 0.01 | 0.02 | 0.02 |
| pr01 | ~~ | un03 | 0.18 | 0.01 | 0.01 | 0.02 | 0.02 |
| fa09 | ~~ | de01 | 0.18 | -0.02 | -0.02 | -0.02 | -0.02 |
| ut05 | ~~ | un09 | 0.18 | -0.02 | -0.02 | -0.02 | -0.02 |
| pr01 | ~~ | pr07 | 0.18 | -0.01 | -0.01 | -0.02 | -0.02 |
| pr08 | ~~ | de01 | 0.17 | -0.01 | -0.01 | -0.02 | -0.02 |
| ut07 | ~~ | un02 | 0.17 | -0.01 | -0.01 | -0.02 | -0.02 |
| ut12 | ~~ | de02 | 0.17 | 0.01 | 0.01 | 0.02 | 0.02 |
| de01 | ~~ | un09 | 0.17 | -0.02 | -0.02 | -0.02 | -0.02 |
| fa05 | ~~ | fa08 | 0.17 | -0.01 | -0.01 | -0.02 | -0.02 |
| ut01 | ~~ | fa02 | 0.17 | -0.01 | -0.01 | -0.02 | -0.02 |
| de07 | ~~ | de10 | 0.17 | -0.01 | -0.01 | -0.02 | -0.02 |
| fa02 | ~~ | un05 | 0.17 | -0.01 | -0.01 | -0.02 | -0.02 |
| co02 | ~~ | fa08 | 0.17 | 0.02 | 0.02 | 0.02 | 0.02 |
| fa05 | ~~ | de08 | 0.17 | 0.01 | 0.01 | 0.02 | 0.02 |
| ut01 | ~~ | un02 | 0.17 | -0.01 | -0.01 | -0.02 | -0.02 |
| pr05 | ~~ | de07 | 0.17 | -0.02 | -0.02 | -0.02 | -0.02 |
| pr01 | ~~ | ut06 | 0.16 | -0.01 | -0.01 | -0.02 | -0.02 |
| ut02 | ~~ | ut05 | 0.16 | -0.01 | -0.01 | -0.02 | -0.02 |
| fa01 | ~~ | un02 | 0.16 | 0.01 | 0.01 | 0.02 | 0.02 |
| pr10 | ~~ | un05 | 0.16 | 0.01 | 0.01 | 0.02 | 0.02 |
| pr05 | ~~ | co08 | 0.16 | 0.02 | 0.02 | 0.02 | 0.02 |
| de08 | ~~ | un03 | 0.16 | 0.01 | 0.01 | 0.02 | 0.02 |
| co06 | ~~ | fa09 | 0.16 | 0.02 | 0.02 | 0.02 | 0.02 |
| co06 | ~~ | ut06 | 0.16 | 0.01 | 0.01 | 0.02 | 0.02 |
| ut04 | ~~ | ut11 | 0.16 | 0.02 | 0.02 | 0.02 | 0.02 |
| co09 | ~~ | ut07 | 0.16 | 0.01 | 0.01 | 0.02 | 0.02 |
| pr08 | ~~ | ut12 | 0.16 | -0.01 | -0.01 | -0.02 | -0.02 |
| ut06 | ~~ | un01 | 0.16 | -0.01 | -0.01 | -0.02 | -0.02 |
| fa09 | ~~ | de02 | 0.16 | -0.02 | -0.02 | -0.02 | -0.02 |
| ut05 | ~~ | un06 | 0.16 | -0.02 | -0.02 | -0.02 | -0.02 |
| co06 | ~~ | de05 | 0.16 | 0.02 | 0.02 | 0.02 | 0.02 |
| pr06 | ~~ | de01 | 0.15 | 0.01 | 0.01 | 0.02 | 0.02 |
| pr02 | ~~ | de10 | 0.15 | -0.01 | -0.01 | -0.02 | -0.02 |
| ut09 | ~~ | de05 | 0.15 | -0.02 | -0.02 | -0.02 | -0.02 |
| pr08 | ~~ | co02 | 0.15 | 0.01 | 0.01 | 0.02 | 0.02 |
| fa08 | ~~ | de03 | 0.15 | -0.02 | -0.02 | -0.02 | -0.02 |
| pr05 | ~~ | co02 | 0.15 | 0.01 | 0.01 | 0.02 | 0.02 |
| fa01 | ~~ | fa05 | 0.15 | -0.01 | -0.01 | -0.04 | -0.04 |
| co08 | ~~ | ut04 | 0.15 | -0.02 | -0.02 | -0.02 | -0.02 |
| co10 | ~~ | un07 | 0.15 | -0.01 | -0.01 | -0.02 | -0.02 |
| de01 | ~~ | un06 | 0.15 | -0.02 | -0.02 | -0.02 | -0.02 |
| pr02 | ~~ | ut05 | 0.15 | -0.01 | -0.01 | -0.02 | -0.02 |
| de01 | ~~ | de10 | 0.15 | -0.01 | -0.01 | -0.02 | -0.02 |
| pr01 | ~~ | de03 | 0.15 | -0.01 | -0.01 | -0.02 | -0.02 |
| co05 | ~~ | ut12 | 0.15 | 0.01 | 0.01 | 0.02 | 0.02 |
| pr01 | ~~ | ut08 | 0.15 | 0.01 | 0.01 | 0.02 | 0.02 |
| pr06 | ~~ | un06 | 0.15 | -0.02 | -0.02 | -0.02 | -0.02 |
| ut11 | ~~ | de09 | 0.14 | -0.02 | -0.02 | -0.02 | -0.02 |
| pr06 | ~~ | un07 | 0.14 | 0.01 | 0.01 | 0.02 | 0.02 |
| co06 | ~~ | co08 | 0.14 | -0.02 | -0.02 | -0.02 | -0.02 |
| fa01 | ~~ | de08 | 0.14 | 0.01 | 0.01 | 0.02 | 0.02 |
| co08 | ~~ | ut12 | 0.14 | -0.01 | -0.01 | -0.02 | -0.02 |
| pr07 | ~~ | fa06 | 0.14 | -0.01 | -0.01 | -0.02 | -0.02 |
| fa01 | ~~ | un03 | 0.14 | -0.01 | -0.01 | -0.02 | -0.02 |
| ut12 | ~~ | un08 | 0.14 | 0.01 | 0.01 | 0.02 | 0.02 |
| ut04 | ~~ | ut06 | 0.14 | -0.01 | -0.01 | -0.02 | -0.02 |
| co09 | ~~ | ut08 | 0.14 | 0.01 | 0.01 | 0.02 | 0.02 |
| pr06 | ~~ | ut09 | 0.14 | -0.01 | -0.01 | -0.02 | -0.02 |
| ut07 | ~~ | fa06 | 0.13 | 0.01 | 0.01 | 0.02 | 0.02 |
| fa09 | ~~ | de05 | 0.13 | 0.02 | 0.02 | 0.02 | 0.02 |
| fa10 | ~~ | de10 | 0.13 | -0.01 | -0.01 | -0.02 | -0.02 |
| pr06 | ~~ | pr07 | 0.13 | 0.01 | 0.01 | 0.02 | 0.02 |
| fa08 | ~~ | un01 | 0.13 | 0.01 | 0.01 | 0.02 | 0.02 |
| ut03 | ~~ | ut12 | 0.13 | -0.01 | -0.01 | -0.02 | -0.02 |
| fa05 | ~~ | un01 | 0.13 | 0.01 | 0.01 | 0.02 | 0.02 |
| ut04 | ~~ | un01 | 0.13 | 0.01 | 0.01 | 0.02 | 0.02 |
| co10 | ~~ | un05 | 0.13 | -0.01 | -0.01 | -0.02 | -0.02 |
| pr05 | ~~ | de08 | 0.13 | -0.01 | -0.01 | -0.02 | -0.02 |
| co03 | ~~ | fa02 | 0.13 | -0.02 | -0.02 | -0.02 | -0.02 |
| pr02 | ~~ | un06 | 0.12 | 0.01 | 0.01 | 0.02 | 0.02 |
| co05 | ~~ | fa08 | 0.12 | -0.01 | -0.01 | -0.02 | -0.02 |
| pr02 | ~~ | de05 | 0.12 | 0.01 | 0.01 | 0.02 | 0.02 |
| pr09 | ~~ | co01 | 0.12 | 0.01 | 0.01 | 0.02 | 0.02 |
| co04 | ~~ | fa06 | 0.12 | -0.01 | -0.01 | -0.02 | -0.02 |
| pr01 | ~~ | ut11 | 0.12 | -0.01 | -0.01 | -0.02 | -0.02 |
| pr07 | ~~ | un09 | 0.12 | 0.01 | 0.01 | 0.02 | 0.02 |
| co05 | ~~ | de06 | 0.12 | -0.01 | -0.01 | -0.02 | -0.02 |
| co09 | ~~ | fa06 | 0.12 | -0.01 | -0.01 | -0.02 | -0.02 |
| pr09 | ~~ | de03 | 0.12 | 0.01 | 0.01 | 0.02 | 0.02 |
| pr01 | ~~ | de08 | 0.12 | -0.01 | -0.01 | -0.02 | -0.02 |
| co06 | ~~ | ut11 | 0.12 | 0.01 | 0.01 | 0.02 | 0.02 |
| de09 | ~~ | un09 | 0.12 | -0.02 | -0.02 | -0.02 | -0.02 |
| un05 | ~~ | un12 | 0.12 | 0.01 | 0.01 | 0.02 | 0.02 |
| pr10 | ~~ | de02 | 0.12 | 0.01 | 0.01 | 0.02 | 0.02 |
| co09 | ~~ | fa05 | 0.11 | -0.01 | -0.01 | -0.02 | -0.02 |
| pr08 | ~~ | un10 | 0.11 | -0.01 | -0.01 | -0.02 | -0.02 |
| ut09 | ~~ | de02 | 0.11 | 0.01 | 0.01 | 0.02 | 0.02 |
| ut08 | ~~ | un04 | 0.11 | -0.01 | -0.01 | -0.02 | -0.02 |
| co09 | ~~ | fa08 | 0.11 | 0.01 | 0.01 | 0.02 | 0.02 |
| co02 | ~~ | ut12 | 0.11 | 0.01 | 0.01 | 0.02 | 0.02 |
| de08 | ~~ | un10 | 0.11 | -0.01 | -0.01 | -0.02 | -0.02 |
| pr01 | ~~ | pr10 | 0.11 | 0.01 | 0.01 | 0.02 | 0.02 |
| co05 | ~~ | fa06 | 0.11 | 0.01 | 0.01 | 0.02 | 0.02 |
| co06 | ~~ | de01 | 0.11 | -0.01 | -0.01 | -0.02 | -0.02 |
| ut07 | ~~ | un10 | 0.11 | -0.01 | -0.01 | -0.02 | -0.02 |
| un02 | ~~ | un05 | 0.11 | -0.01 | -0.01 | -0.02 | -0.02 |
| ut08 | ~~ | un05 | 0.10 | 0.01 | 0.01 | 0.02 | 0.02 |
| fa05 | ~~ | un09 | 0.10 | 0.01 | 0.01 | 0.02 | 0.02 |
| fa09 | ~~ | un05 | 0.10 | -0.01 | -0.01 | -0.02 | -0.02 |
| fa08 | ~~ | un05 | 0.10 | 0.01 | 0.01 | 0.02 | 0.02 |
| fa10 | ~~ | de01 | 0.10 | 0.01 | 0.01 | 0.02 | 0.02 |
| ut12 | ~~ | un02 | 0.10 | -0.01 | -0.01 | -0.02 | -0.02 |
| ut03 | ~~ | fa05 | 0.10 | 0.01 | 0.01 | 0.02 | 0.02 |
| ut03 | ~~ | un02 | 0.10 | 0.01 | 0.01 | 0.02 | 0.02 |
| pr02 | ~~ | un04 | 0.10 | 0.01 | 0.01 | 0.02 | 0.02 |
| ut05 | ~~ | fa05 | 0.10 | 0.01 | 0.01 | 0.02 | 0.02 |
| ut02 | ~~ | ut09 | 0.10 | 0.01 | 0.01 | 0.02 | 0.02 |
| pr05 | ~~ | co06 | 0.10 | 0.01 | 0.01 | 0.01 | 0.01 |
| co10 | ~~ | un04 | 0.10 | -0.01 | -0.01 | -0.02 | -0.02 |
| pr02 | ~~ | pr10 | 0.09 | 0.01 | 0.01 | 0.01 | 0.01 |
| fa02 | ~~ | de03 | 0.09 | 0.01 | 0.01 | 0.01 | 0.01 |
| co06 | ~~ | un04 | 0.09 | 0.01 | 0.01 | 0.01 | 0.01 |
| pr01 | ~~ | fa05 | 0.09 | -0.01 | -0.01 | -0.02 | -0.02 |
| co02 | ~~ | un08 | 0.09 | -0.01 | -0.01 | -0.01 | -0.01 |
| co09 | ~~ | ut11 | 0.09 | 0.01 | 0.01 | 0.01 | 0.01 |
| fa06 | ~~ | un04 | 0.09 | 0.01 | 0.01 | 0.01 | 0.01 |
| pr07 | ~~ | fa09 | 0.09 | 0.01 | 0.01 | 0.01 | 0.01 |
| fa02 | ~~ | de01 | 0.09 | -0.01 | -0.01 | -0.01 | -0.01 |
| co08 | ~~ | ut09 | 0.09 | 0.01 | 0.01 | 0.01 | 0.01 |
| co10 | ~~ | ut04 | 0.09 | -0.01 | -0.01 | -0.01 | -0.01 |
| fa10 | ~~ | de08 | 0.09 | -0.01 | -0.01 | -0.01 | -0.01 |
| pr06 | ~~ | pr09 | 0.09 | -0.01 | -0.01 | -0.01 | -0.01 |
| co01 | ~~ | co03 | 0.09 | -0.01 | -0.01 | -0.01 | -0.01 |
| pr05 | ~~ | ut09 | 0.08 | 0.01 | 0.01 | 0.01 | 0.01 |
| pr01 | ~~ | co01 | 0.08 | 0.01 | 0.01 | 0.02 | 0.02 |
| co05 | ~~ | fa05 | 0.08 | 0.01 | 0.01 | 0.02 | 0.02 |
| co10 | ~~ | de02 | 0.08 | 0.01 | 0.01 | 0.01 | 0.01 |
| pr05 | ~~ | de03 | 0.08 | -0.01 | -0.01 | -0.01 | -0.01 |
| co09 | ~~ | un02 | 0.08 | -0.01 | -0.01 | -0.01 | -0.01 |
| ut02 | ~~ | un08 | 0.08 | 0.01 | 0.01 | 0.01 | 0.01 |
| co10 | ~~ | de09 | 0.08 | -0.01 | -0.01 | -0.01 | -0.01 |
| co02 | ~~ | un03 | 0.08 | -0.01 | -0.01 | -0.01 | -0.01 |
| ut07 | ~~ | un12 | 0.08 | 0.01 | 0.01 | 0.01 | 0.01 |
| ut12 | ~~ | de08 | 0.08 | 0.01 | 0.01 | 0.01 | 0.01 |
| co06 | ~~ | ut04 | 0.08 | -0.01 | -0.01 | -0.01 | -0.01 |
| pr02 | ~~ | ut12 | 0.08 | 0.01 | 0.01 | 0.01 | 0.01 |
| ut02 | ~~ | fa05 | 0.08 | -0.01 | -0.01 | -0.02 | -0.02 |
| ut04 | ~~ | de05 | 0.08 | -0.01 | -0.01 | -0.01 | -0.01 |
| ut01 | ~~ | un08 | 0.07 | 0.01 | 0.01 | 0.01 | 0.01 |
| pr06 | ~~ | ut04 | 0.07 | 0.01 | 0.01 | 0.01 | 0.01 |
| co03 | ~~ | un09 | 0.07 | -0.01 | -0.01 | -0.01 | -0.01 |
| co06 | ~~ | de06 | 0.07 | 0.01 | 0.01 | 0.01 | 0.01 |
| ut01 | ~~ | un06 | 0.07 | -0.01 | -0.01 | -0.01 | -0.01 |
| pr06 | ~~ | ut07 | 0.07 | 0.01 | 0.01 | 0.01 | 0.01 |
| pr10 | ~~ | de07 | 0.07 | -0.01 | -0.01 | -0.01 | -0.01 |
| de03 | ~~ | un08 | 0.07 | 0.01 | 0.01 | 0.01 | 0.01 |
| fa06 | ~~ | de03 | 0.07 | -0.01 | -0.01 | -0.01 | -0.01 |
| pr01 | ~~ | un05 | 0.07 | 0.01 | 0.01 | 0.01 | 0.01 |
| co05 | ~~ | de03 | 0.07 | -0.01 | -0.01 | -0.01 | -0.01 |
| ut06 | ~~ | fa10 | 0.07 | -0.01 | -0.01 | -0.01 | -0.01 |
| ut04 | ~~ | un07 | 0.06 | 0.01 | 0.01 | 0.01 | 0.01 |
| co03 | ~~ | un11 | 0.06 | -0.01 | -0.01 | -0.01 | -0.01 |
| fa06 | ~~ | de01 | 0.06 | 0.01 | 0.01 | 0.01 | 0.01 |
| ut05 | ~~ | un03 | 0.06 | -0.01 | -0.01 | -0.01 | -0.01 |
| ut04 | ~~ | un03 | 0.06 | -0.01 | -0.01 | -0.01 | -0.01 |
| ut11 | ~~ | fa01 | 0.06 | 0.01 | 0.01 | 0.01 | 0.01 |
| pr07 | ~~ | co06 | 0.06 | -0.01 | -0.01 | -0.01 | -0.01 |
| un09 | ~~ | un12 | 0.06 | 0.01 | 0.01 | 0.01 | 0.01 |
| ut03 | ~~ | fa10 | 0.06 | 0.01 | 0.01 | 0.01 | 0.01 |
| co02 | ~~ | un01 | 0.06 | 0.01 | 0.01 | 0.01 | 0.01 |
| co06 | ~~ | de08 | 0.06 | 0.01 | 0.01 | 0.01 | 0.01 |
| co02 | ~~ | ut08 | 0.06 | 0.01 | 0.01 | 0.01 | 0.01 |
| co08 | ~~ | ut11 | 0.06 | -0.01 | -0.01 | -0.01 | -0.01 |
| co08 | ~~ | un04 | 0.06 | 0.01 | 0.01 | 0.01 | 0.01 |
| pr10 | ~~ | ut05 | 0.06 | -0.01 | -0.01 | -0.01 | -0.01 |
| co05 | ~~ | ut08 | 0.06 | -0.01 | -0.01 | -0.01 | -0.01 |
| co01 | ~~ | un05 | 0.05 | -0.01 | -0.01 | -0.01 | -0.01 |
| pr10 | ~~ | un06 | 0.05 | -0.01 | -0.01 | -0.01 | -0.01 |
| pr10 | ~~ | un01 | 0.05 | 0.01 | 0.01 | 0.01 | 0.01 |
| co02 | ~~ | fa10 | 0.05 | 0.01 | 0.01 | 0.01 | 0.01 |
| co08 | ~~ | un02 | 0.05 | -0.01 | -0.01 | -0.01 | -0.01 |
| fa02 | ~~ | fa05 | 0.05 | -0.01 | -0.01 | -0.01 | -0.01 |
| co06 | ~~ | un12 | 0.05 | 0.01 | 0.01 | 0.01 | 0.01 |
| pr06 | ~~ | ut12 | 0.05 | -0.01 | -0.01 | -0.01 | -0.01 |
| pr07 | ~~ | fa01 | 0.05 | -0.01 | -0.01 | -0.01 | -0.01 |
| co01 | ~~ | co08 | 0.05 | 0.01 | 0.01 | 0.01 | 0.01 |
| de08 | ~~ | un02 | 0.05 | -0.01 | -0.01 | -0.01 | -0.01 |
| ut11 | ~~ | de02 | 0.05 | 0.01 | 0.01 | 0.01 | 0.01 |
| pr02 | ~~ | fa06 | 0.05 | -0.01 | -0.01 | -0.01 | -0.01 |
| co09 | ~~ | un03 | 0.04 | -0.01 | -0.01 | -0.01 | -0.01 |
| pr08 | ~~ | ut09 | 0.04 | -0.01 | -0.01 | -0.01 | -0.01 |
| pr06 | ~~ | fa06 | 0.04 | -0.01 | -0.01 | -0.01 | -0.01 |
| ut11 | ~~ | un02 | 0.04 | -0.01 | -0.01 | -0.01 | -0.01 |
| pr06 | ~~ | ut05 | 0.04 | -0.01 | -0.01 | -0.01 | -0.01 |
| pr09 | ~~ | pr10 | 0.04 | -0.01 | -0.01 | -0.01 | -0.01 |
| ut06 | ~~ | fa06 | 0.04 | -0.01 | -0.01 | -0.01 | -0.01 |
| pr02 | ~~ | de02 | 0.04 | 0.01 | 0.01 | 0.01 | 0.01 |
| ut12 | ~~ | de09 | 0.04 | 0.01 | 0.01 | 0.01 | 0.01 |
| pr08 | ~~ | ut06 | 0.04 | 0.00 | 0.00 | 0.01 | 0.01 |
| ut12 | ~~ | un06 | 0.04 | 0.01 | 0.01 | 0.01 | 0.01 |
| pr07 | ~~ | un07 | 0.04 | 0.01 | 0.01 | 0.01 | 0.01 |
| un02 | ~~ | un10 | 0.04 | 0.01 | 0.01 | 0.01 | 0.01 |
| ut12 | ~~ | de05 | 0.04 | -0.01 | -0.01 | -0.01 | -0.01 |
| ut02 | ~~ | fa08 | 0.04 | 0.01 | 0.01 | 0.01 | 0.01 |
| co03 | ~~ | ut05 | 0.04 | -0.01 | -0.01 | -0.01 | -0.01 |
| ut06 | ~~ | de09 | 0.04 | 0.01 | 0.01 | 0.01 | 0.01 |
| pr02 | ~~ | ut01 | 0.04 | 0.00 | 0.00 | -0.01 | -0.01 |
| pr02 | ~~ | fa09 | 0.04 | -0.01 | -0.01 | -0.01 | -0.01 |
| ut09 | ~~ | fa09 | 0.03 | -0.01 | -0.01 | -0.01 | -0.01 |
| pr01 | ~~ | ut07 | 0.03 | -0.01 | -0.01 | -0.01 | -0.01 |
| fa06 | ~~ | de06 | 0.03 | 0.01 | 0.01 | 0.01 | 0.01 |
| fa02 | ~~ | un12 | 0.03 | -0.01 | -0.01 | -0.01 | -0.01 |
| co02 | ~~ | fa02 | 0.03 | 0.01 | 0.01 | 0.01 | 0.01 |
| co09 | ~~ | fa09 | 0.03 | -0.01 | -0.01 | -0.01 | -0.01 |
| co02 | ~~ | un05 | 0.03 | 0.00 | 0.00 | 0.01 | 0.01 |
| co01 | ~~ | un07 | 0.03 | 0.01 | 0.01 | 0.01 | 0.01 |
| pr01 | ~~ | ut01 | 0.03 | 0.00 | 0.00 | -0.01 | -0.01 |
| co10 | ~~ | de01 | 0.03 | 0.01 | 0.01 | 0.01 | 0.01 |
| pr09 | ~~ | un07 | 0.03 | 0.01 | 0.01 | 0.01 | 0.01 |
| pr02 | ~~ | un11 | 0.03 | 0.01 | 0.01 | 0.01 | 0.01 |
| ut09 | ~~ | fa10 | 0.03 | 0.01 | 0.01 | 0.01 | 0.01 |
| co01 | ~~ | ut02 | 0.03 | 0.00 | 0.00 | -0.01 | -0.01 |
| ut06 | ~~ | de06 | 0.03 | 0.01 | 0.01 | 0.01 | 0.01 |
| ut08 | ~~ | ut12 | 0.03 | 0.01 | 0.01 | 0.01 | 0.01 |
| de06 | ~~ | un10 | 0.03 | -0.01 | -0.01 | -0.01 | -0.01 |
| co02 | ~~ | un11 | 0.03 | 0.00 | 0.00 | -0.01 | -0.01 |
| ut05 | ~~ | ut09 | 0.03 | 0.01 | 0.01 | 0.01 | 0.01 |
| ut05 | ~~ | de02 | 0.03 | 0.01 | 0.01 | 0.01 | 0.01 |
| co10 | ~~ | ut05 | 0.03 | 0.01 | 0.01 | 0.01 | 0.01 |
| ut03 | ~~ | ut04 | 0.03 | -0.01 | -0.01 | -0.01 | -0.01 |
| ut06 | ~~ | un04 | 0.03 | 0.00 | 0.00 | -0.01 | -0.01 |
| pr06 | ~~ | co08 | 0.03 | 0.01 | 0.01 | 0.01 | 0.01 |
| pr10 | ~~ | co09 | 0.03 | -0.01 | -0.01 | -0.01 | -0.01 |
| co10 | ~~ | un09 | 0.03 | -0.01 | -0.01 | -0.01 | -0.01 |
| pr07 | ~~ | un06 | 0.03 | -0.01 | -0.01 | -0.01 | -0.01 |
| fa01 | ~~ | de09 | 0.02 | 0.01 | 0.01 | 0.01 | 0.01 |
| pr07 | ~~ | co01 | 0.02 | 0.00 | 0.00 | 0.01 | 0.01 |
| pr05 | ~~ | co05 | 0.02 | 0.01 | 0.01 | 0.01 | 0.01 |
| co10 | ~~ | un03 | 0.02 | 0.01 | 0.01 | 0.01 | 0.01 |
| pr10 | ~~ | ut08 | 0.02 | -0.01 | -0.01 | -0.01 | -0.01 |
| co05 | ~~ | ut03 | 0.02 | -0.01 | -0.01 | -0.01 | -0.01 |
| ut02 | ~~ | fa10 | 0.02 | 0.00 | 0.00 | -0.01 | -0.01 |
| ut09 | ~~ | un12 | 0.02 | 0.00 | 0.00 | -0.01 | -0.01 |
| pr02 | ~~ | co04 | 0.02 | 0.01 | 0.01 | 0.01 | 0.01 |
| ut03 | ~~ | fa06 | 0.02 | 0.01 | 0.01 | 0.01 | 0.01 |
| pr06 | ~~ | co01 | 0.02 | 0.00 | 0.00 | 0.01 | 0.01 |
| de03 | ~~ | un06 | 0.02 | 0.01 | 0.01 | 0.01 | 0.01 |
| co09 | ~~ | ut09 | 0.02 | 0.00 | 0.00 | -0.01 | -0.01 |
| ut05 | ~~ | fa02 | 0.02 | -0.01 | -0.01 | -0.01 | -0.01 |
| co01 | ~~ | fa01 | 0.02 | 0.00 | 0.00 | -0.01 | -0.01 |
| pr10 | ~~ | co08 | 0.02 | 0.01 | 0.01 | 0.01 | 0.01 |
| fa05 | ~~ | de06 | 0.02 | 0.00 | 0.00 | 0.01 | 0.01 |
| pr05 | ~~ | ut04 | 0.02 | -0.01 | -0.01 | -0.01 | -0.01 |
| pr06 | ~~ | un10 | 0.02 | 0.00 | 0.00 | -0.01 | -0.01 |
| fa04 | ~~ | un06 | 0.02 | 0.01 | 0.01 | 0.01 | 0.01 |
| pr02 | ~~ | un05 | 0.02 | 0.00 | 0.00 | -0.01 | -0.01 |
| ut01 | ~~ | de03 | 0.02 | 0.00 | 0.00 | -0.01 | -0.01 |
| pr07 | ~~ | co10 | 0.02 | 0.00 | 0.00 | -0.01 | -0.01 |
| ut06 | ~~ | de03 | 0.02 | 0.00 | 0.00 | -0.01 | -0.01 |
| pr06 | ~~ | pr10 | 0.02 | 0.00 | 0.00 | -0.01 | -0.01 |
| ut02 | ~~ | de06 | 0.02 | 0.00 | 0.00 | 0.01 | 0.01 |
| de05 | ~~ | un04 | 0.02 | 0.00 | 0.00 | 0.01 | 0.01 |
| de06 | ~~ | un08 | 0.02 | 0.00 | 0.00 | -0.01 | -0.01 |
| fa08 | ~~ | un10 | 0.02 | 0.01 | 0.01 | 0.01 | 0.01 |
| ut02 | ~~ | un04 | 0.02 | 0.00 | 0.00 | -0.01 | -0.01 |
| ut04 | ~~ | ut09 | 0.02 | -0.01 | -0.01 | -0.01 | -0.01 |
| pr05 | ~~ | co10 | 0.02 | -0.01 | -0.01 | -0.01 | -0.01 |
| fa06 | ~~ | de08 | 0.02 | 0.00 | 0.00 | 0.01 | 0.01 |
| ut09 | ~~ | un07 | 0.02 | 0.00 | 0.00 | 0.01 | 0.01 |
| pr10 | ~~ | fa09 | 0.02 | -0.01 | -0.01 | -0.01 | -0.01 |
| ut09 | ~~ | de01 | 0.01 | 0.00 | 0.00 | 0.01 | 0.01 |
| co02 | ~~ | de01 | 0.01 | 0.00 | 0.00 | -0.01 | -0.01 |
| ut12 | ~~ | fa04 | 0.01 | 0.00 | 0.00 | 0.01 | 0.01 |
| co02 | ~~ | de05 | 0.01 | 0.00 | 0.00 | -0.01 | -0.01 |
| ut11 | ~~ | fa09 | 0.01 | 0.01 | 0.01 | 0.01 | 0.01 |
| fa06 | ~~ | de09 | 0.01 | 0.01 | 0.01 | 0.01 | 0.01 |
| ut07 | ~~ | un11 | 0.01 | 0.00 | 0.00 | -0.01 | -0.01 |
| ut02 | ~~ | un02 | 0.01 | 0.00 | 0.00 | 0.01 | 0.01 |
| ut05 | ~~ | ut07 | 0.01 | 0.00 | 0.00 | 0.00 | 0.00 |
| co01 | ~~ | co04 | 0.01 | 0.00 | 0.00 | 0.00 | 0.00 |
| ut07 | ~~ | fa04 | 0.01 | 0.00 | 0.00 | 0.00 | 0.00 |
| pr07 | ~~ | ut11 | 0.01 | 0.00 | 0.00 | 0.00 | 0.00 |
| pr07 | ~~ | de07 | 0.01 | 0.00 | 0.00 | 0.00 | 0.00 |
| co02 | ~~ | un04 | 0.01 | 0.00 | 0.00 | 0.00 | 0.00 |
| fa01 | ~~ | un04 | 0.01 | 0.00 | 0.00 | 0.01 | 0.01 |
| ut03 | ~~ | de07 | 0.01 | 0.00 | 0.00 | 0.00 | 0.00 |
| ut12 | ~~ | un12 | 0.01 | 0.00 | 0.00 | 0.00 | 0.00 |
| co01 | ~~ | de05 | 0.01 | 0.00 | 0.00 | 0.00 | 0.00 |
| co10 | ~~ | un06 | 0.01 | 0.00 | 0.00 | 0.00 | 0.00 |
| ut07 | ~~ | un01 | 0.01 | 0.00 | 0.00 | 0.00 | 0.00 |
| ut12 | ~~ | fa05 | 0.01 | 0.00 | 0.00 | 0.00 | 0.00 |
| pr05 | ~~ | ut02 | 0.01 | 0.00 | 0.00 | 0.00 | 0.00 |
| pr07 | ~~ | un08 | 0.01 | 0.00 | 0.00 | 0.00 | 0.00 |
| co06 | ~~ | un06 | 0.01 | 0.00 | 0.00 | 0.00 | 0.00 |
| co10 | ~~ | ut09 | 0.01 | 0.00 | 0.00 | 0.00 | 0.00 |
| pr10 | ~~ | ut12 | 0.01 | 0.00 | 0.00 | 0.00 | 0.00 |
| de08 | ~~ | de09 | 0.01 | 0.00 | 0.00 | 0.00 | 0.00 |
| de08 | ~~ | un05 | 0.01 | 0.00 | 0.00 | 0.00 | 0.00 |
| pr07 | ~~ | ut05 | 0.01 | 0.00 | 0.00 | 0.00 | 0.00 |
| un04 | ~~ | un09 | 0.01 | 0.00 | 0.00 | 0.00 | 0.00 |
| co09 | ~~ | fa10 | 0.01 | 0.00 | 0.00 | 0.00 | 0.00 |
| pr10 | ~~ | de09 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| de07 | ~~ | un10 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| un06 | ~~ | un11 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| ut04 | ~~ | fa09 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| ut04 | ~~ | de06 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| co02 | ~~ | co03 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| ut06 | ~~ | ut12 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| ut04 | ~~ | fa10 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| pr06 | ~~ | co09 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| de10 | ~~ | un06 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| pr02 | ~~ | ut06 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| un06 | ~~ | un09 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| fa06 | ~~ | un11 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| ut03 | ~~ | un08 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| pr07 | ~~ | ut03 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| pr10 | ~~ | un07 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| pr08 | ~~ | de08 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| pr01 | ~~ | fa09 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| fa08 | ~~ | de02 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| pr02 | ~~ | co03 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| fa01 | ~~ | de06 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| fa01 | ~~ | un07 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| fa06 | ~~ | de05 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| fa04 | ~~ | de08 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| co01 | ~~ | de02 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| fa05 | ~~ | un12 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| co01 | ~~ | fa08 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| ut08 | ~~ | un06 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| pr06 | ~~ | un11 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| fa08 | ~~ | de10 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| ut05 | ~~ | un08 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| pr07 | ~~ | pr10 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| co06 | ~~ | un07 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| co02 | ~~ | un06 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| co02 | ~~ | de07 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| co03 | ~~ | ut01 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| ut02 | ~~ | un05 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| co06 | ~~ | de03 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| co01 | ~~ | co10 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| co05 | ~~ | de08 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| ut08 | ~~ | de05 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| co02 | ~~ | fa09 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| co02 | ~~ | de09 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| co10 | ~~ | ut11 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| ut05 | ~~ | fa04 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| pr02 | ~~ | co08 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| de02 | ~~ | un03 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| co06 | ~~ | de02 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| ut06 | ~~ | fa09 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| co06 | ~~ | ut07 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| ut04 | ~~ | fa01 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| co06 | ~~ | fa05 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| co10 | ~~ | un11 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| pr10 | ~~ | ut11 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| de01 | ~~ | de07 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
Factors correlations:
modif3 %>%
filter(op == "~~" &
lhs %in% c("PR", "CO", "UT", "FA", "DE", "UN") &
rhs %in% c("PR", "CO", "UT", "FA", "DE", "UN")) %>%
kable(digits = 2,
col.names = c("Factor", "", "Factor", "Modification Index", "epc", "sepc.lv", "sepc.all", "sepc.nox"))| Factor | Factor | Modification Index | epc | sepc.lv | sepc.all | sepc.nox | |
|---|---|---|---|---|---|---|---|
| FA | ~~ | UN | 18.86 | -0.12 | -0.24 | -0.24 | -0.24 |
| PR | ~~ | UT | 16.05 | 0.07 | 0.62 | 0.62 | 0.62 |
| CO | ~~ | UT | 13.65 | -0.07 | -0.24 | -0.24 | -0.24 |
| FA | ~~ | DE | 12.35 | 0.06 | 0.38 | 0.38 | 0.38 |
| PR | ~~ | DE | 9.73 | -0.05 | -1.22 | -1.22 | -1.22 |
| PR | ~~ | FA | 7.60 | -0.06 | -0.36 | -0.36 | -0.36 |
| CO | ~~ | FA | 2.64 | 0.04 | 0.10 | 0.10 | 0.10 |
| DE | ~~ | UN | 1.88 | 0.02 | 0.13 | 0.13 | 0.13 |
| UT | ~~ | DE | 1.58 | -0.02 | -0.16 | -0.16 | -0.16 |
| PR | ~~ | CO | 1.02 | 0.02 | 0.15 | 0.15 | 0.15 |
| CO | ~~ | DE | 0.67 | 0.01 | 0.10 | 0.10 | 0.10 |
| PR | ~~ | UN | 0.55 | 0.01 | 0.08 | 0.08 | 0.08 |
| UT | ~~ | FA | 0.23 | -0.01 | -0.03 | -0.03 | -0.03 |
| UT | ~~ | UN | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| CO | ~~ | UN | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
mdl4 <- "
PR =~ pr01 + pr02 + pr05 + pr06 + pr07 + pr08 + pr09 + pr10
CO =~ co01 + co02 + co03 + co04 + co05 + co06 + co08 + co09 + co10
UT =~ ut01 + ut02 + ut03 + ut04 + ut05 + ut06 + ut07 + ut08 + ut09 + ut11 + ut12
FA =~ fa01 + fa02 + fa04 + fa05 + fa06 + fa08 + fa09 + fa10
DE =~ de01 + de02 + de03 + de05 + de06 + de07 + de08 + de09 + de10
UN =~ un01 + un02 + un03 + un04 + un05 + un06 + un07 + un08 + un09 + un10 + un11 + un12
DT =~ PR + CO + UT + FA + DE + UN
de05 ~~ de09
pr05 ~~ de06
fa02 ~~ fa08
pr05 ~~ ut11
fa02 ~~ fa09
fa08 ~~ fa09
ut11 ~~ de06
"## lavaan 0.6-8 ended normally after 48 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 127
##
## Number of observations 495
##
## Model Test User Model:
##
## Test statistic 4321.703
## Degrees of freedom 1526
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## PR =~
## pr01 1.000
## pr02 0.728 0.056 13.027 0.000
## pr05 0.717 0.063 11.369 0.000
## pr06 0.811 0.062 13.157 0.000
## pr07 1.054 0.062 17.060 0.000
## pr08 0.859 0.050 17.015 0.000
## pr09 0.731 0.055 13.317 0.000
## pr10 0.668 0.061 11.018 0.000
## CO =~
## co01 1.000
## co02 0.892 0.061 14.528 0.000
## co03 0.651 0.061 10.716 0.000
## co04 0.475 0.065 7.307 0.000
## co05 1.085 0.065 16.579 0.000
## co06 0.859 0.066 13.092 0.000
## co08 0.435 0.062 6.992 0.000
## co09 0.973 0.063 15.443 0.000
## co10 0.833 0.065 12.835 0.000
## UT =~
## ut01 1.000
## ut02 1.075 0.054 19.729 0.000
## ut03 0.668 0.062 10.843 0.000
## ut04 0.634 0.062 10.237 0.000
## ut05 0.967 0.065 14.801 0.000
## ut06 1.006 0.058 17.389 0.000
## ut07 0.796 0.062 12.825 0.000
## ut08 0.808 0.057 14.153 0.000
## ut09 0.946 0.063 14.994 0.000
## ut11 0.748 0.063 11.840 0.000
## ut12 0.978 0.061 15.911 0.000
## FA =~
## fa01 1.000
## fa02 0.416 0.061 6.865 0.000
## fa04 0.431 0.055 7.804 0.000
## fa05 1.017 0.051 20.123 0.000
## fa06 0.736 0.052 14.240 0.000
## fa08 0.374 0.058 6.391 0.000
## fa09 0.522 0.060 8.737 0.000
## fa10 0.755 0.058 12.950 0.000
## DE =~
## de01 1.000
## de02 1.271 0.106 12.039 0.000
## de03 1.149 0.105 10.967 0.000
## de05 0.939 0.098 9.611 0.000
## de06 1.001 0.096 10.446 0.000
## de07 0.958 0.088 10.934 0.000
## de08 1.146 0.096 11.925 0.000
## de09 0.498 0.093 5.370 0.000
## de10 1.319 0.109 12.146 0.000
## UN =~
## un01 1.000
## un02 1.212 0.064 18.941 0.000
## un03 0.811 0.068 11.920 0.000
## un04 1.022 0.062 16.533 0.000
## un05 1.140 0.062 18.334 0.000
## un06 0.779 0.072 10.820 0.000
## un07 1.035 0.067 15.354 0.000
## un08 1.118 0.066 16.979 0.000
## un09 1.084 0.070 15.410 0.000
## un10 1.046 0.066 15.909 0.000
## un11 1.188 0.068 17.435 0.000
## un12 1.061 0.064 16.565 0.000
## DT =~
## PR 1.000
## CO 0.769 0.062 12.436 0.000
## UT 0.829 0.061 13.615 0.000
## FA 0.836 0.066 12.631 0.000
## DE 0.809 0.067 12.017 0.000
## UN 0.417 0.054 7.689 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## .de05 ~~
## .de09 0.701 0.062 11.257 0.000
## .pr05 ~~
## .de06 0.601 0.054 11.139 0.000
## .fa02 ~~
## .fa08 0.645 0.063 10.288 0.000
## .pr05 ~~
## .ut11 0.581 0.056 10.365 0.000
## .fa02 ~~
## .fa09 0.611 0.062 9.808 0.000
## .fa08 ~~
## .fa09 0.562 0.060 9.417 0.000
## .ut11 ~~
## .de06 0.518 0.054 9.624 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .pr01 0.373 0.029 12.957 0.000
## .pr02 0.622 0.042 14.867 0.000
## .pr05 1.022 0.067 15.227 0.000
## .pr06 0.752 0.051 14.843 0.000
## .pr07 0.570 0.042 13.729 0.000
## .pr08 0.382 0.028 13.748 0.000
## .pr09 0.590 0.040 14.813 0.000
## .pr10 0.805 0.053 15.170 0.000
## .co01 0.525 0.040 13.109 0.000
## .co02 0.578 0.042 13.839 0.000
## .co03 0.775 0.052 14.985 0.000
## .co04 1.036 0.067 15.436 0.000
## .co05 0.486 0.039 12.396 0.000
## .co06 0.760 0.053 14.404 0.000
## .co08 0.958 0.062 15.464 0.000
## .co09 0.542 0.041 13.327 0.000
## .co10 0.760 0.052 14.484 0.000
## .ut01 0.440 0.033 13.477 0.000
## .ut02 0.341 0.028 12.367 0.000
## .ut03 0.929 0.061 15.258 0.000
## .ut04 0.960 0.063 15.320 0.000
## .ut05 0.840 0.057 14.633 0.000
## .ut06 0.527 0.038 13.827 0.000
## .ut07 0.858 0.057 15.003 0.000
## .ut08 0.670 0.045 14.771 0.000
## .ut09 0.773 0.053 14.588 0.000
## .ut11 1.119 0.073 15.242 0.000
## .ut12 0.683 0.048 14.347 0.000
## .fa01 0.371 0.036 10.220 0.000
## .fa02 1.245 0.080 15.465 0.000
## .fa04 1.019 0.066 15.385 0.000
## .fa05 0.343 0.036 9.613 0.000
## .fa06 0.708 0.050 14.250 0.000
## .fa08 1.171 0.076 15.502 0.000
## .fa09 1.162 0.076 15.285 0.000
## .fa10 0.958 0.066 14.588 0.000
## .de01 0.794 0.054 14.778 0.000
## .de02 0.676 0.049 13.906 0.000
## .de03 0.889 0.061 14.605 0.000
## .de05 0.992 0.066 15.059 0.000
## .de06 0.964 0.064 14.947 0.000
## .de07 0.626 0.043 14.620 0.000
## .de08 0.581 0.041 14.007 0.000
## .de09 1.334 0.086 15.587 0.000
## .de10 0.690 0.050 13.803 0.000
## .un01 0.497 0.035 14.366 0.000
## .un02 0.399 0.030 13.228 0.000
## .un03 0.966 0.063 15.272 0.000
## .un04 0.541 0.038 14.423 0.000
## .un05 0.423 0.031 13.641 0.000
## .un06 1.149 0.075 15.375 0.000
## .un07 0.731 0.050 14.739 0.000
## .un08 0.581 0.041 14.271 0.000
## .un09 0.792 0.054 14.726 0.000
## .un10 0.657 0.045 14.603 0.000
## .un11 0.584 0.041 14.092 0.000
## .un12 0.578 0.040 14.412 0.000
## .PR 0.059 0.017 3.421 0.001
## .CO 0.314 0.038 8.159 0.000
## .UT 0.291 0.033 8.734 0.000
## .FA 0.460 0.049 9.428 0.000
## .DE 0.051 0.014 3.767 0.000
## .UN 0.514 0.055 9.332 0.000
## DT 0.540 0.057 9.408 0.000
tibble(
`Model 4` = c(
"Chi-Squared",
"DF",
"p",
"GFI",
"AGFI",
"CFI",
"TLI",
"SRMR",
"RMSEA"
),
Value = round(fitmeasures(
model4,
c(
"chisq",
"df",
"pvalue",
"gfi",
"agfi",
"cfi",
"tli",
"srmr",
"rmsea"
)
), 4)
) %>% kable()| Model 4 | Value |
|---|---|
| Chi-Squared | 4321.7034 |
| DF | 1526.0000 |
| p | 0.0000 |
| GFI | 0.7210 |
| AGFI | 0.6978 |
| CFI | 0.8085 |
| TLI | 0.7997 |
| SRMR | 0.0961 |
| RMSEA | 0.0608 |
Standardized solution:
Loadings:
smodel4 %>%
filter(op == "=~") %>%
kable(
col.names = c(
"Factor",
"",
"Item",
"Loading",
"SE",
"z",
"p",
"CI lower bound",
"CI upper bound"
),
digits = 3
)| Factor | Item | Loading | SE | z | p | CI lower bound | CI upper bound | |
|---|---|---|---|---|---|---|---|---|
| PR | =~ | pr01 | 0.785 | 0.020 | 38.709 | 0 | 0.745 | 0.825 |
| PR | =~ | pr02 | 0.581 | 0.032 | 18.004 | 0 | 0.518 | 0.645 |
| PR | =~ | pr05 | 0.481 | 0.035 | 13.946 | 0 | 0.414 | 0.549 |
| PR | =~ | pr06 | 0.586 | 0.032 | 18.316 | 0 | 0.524 | 0.649 |
| PR | =~ | pr07 | 0.734 | 0.024 | 31.182 | 0 | 0.688 | 0.780 |
| PR | =~ | pr08 | 0.732 | 0.024 | 30.979 | 0 | 0.686 | 0.779 |
| PR | =~ | pr09 | 0.593 | 0.032 | 18.705 | 0 | 0.531 | 0.655 |
| PR | =~ | pr10 | 0.499 | 0.036 | 13.800 | 0 | 0.429 | 0.570 |
| CO | =~ | co01 | 0.740 | 0.024 | 30.666 | 0 | 0.692 | 0.787 |
| CO | =~ | co02 | 0.683 | 0.027 | 24.845 | 0 | 0.629 | 0.737 |
| CO | =~ | co03 | 0.508 | 0.037 | 13.897 | 0 | 0.436 | 0.579 |
| CO | =~ | co04 | 0.348 | 0.043 | 8.185 | 0 | 0.265 | 0.432 |
| CO | =~ | co05 | 0.778 | 0.022 | 35.752 | 0 | 0.735 | 0.821 |
| CO | =~ | co06 | 0.617 | 0.031 | 19.807 | 0 | 0.556 | 0.678 |
| CO | =~ | co08 | 0.333 | 0.043 | 7.754 | 0 | 0.249 | 0.418 |
| CO | =~ | co09 | 0.725 | 0.025 | 28.999 | 0 | 0.676 | 0.774 |
| CO | =~ | co10 | 0.605 | 0.032 | 19.048 | 0 | 0.543 | 0.668 |
| UT | =~ | ut01 | 0.775 | 0.020 | 37.826 | 0 | 0.735 | 0.815 |
| UT | =~ | ut02 | 0.832 | 0.017 | 49.845 | 0 | 0.799 | 0.864 |
| UT | =~ | ut03 | 0.491 | 0.036 | 13.553 | 0 | 0.420 | 0.562 |
| UT | =~ | ut04 | 0.466 | 0.037 | 12.469 | 0 | 0.392 | 0.539 |
| UT | =~ | ut05 | 0.651 | 0.028 | 23.122 | 0 | 0.596 | 0.706 |
| UT | =~ | ut06 | 0.748 | 0.022 | 33.641 | 0 | 0.705 | 0.792 |
| UT | =~ | ut07 | 0.573 | 0.032 | 17.696 | 0 | 0.510 | 0.637 |
| UT | =~ | ut08 | 0.626 | 0.030 | 21.160 | 0 | 0.568 | 0.684 |
| UT | =~ | ut09 | 0.659 | 0.028 | 23.749 | 0 | 0.604 | 0.713 |
| UT | =~ | ut11 | 0.499 | 0.034 | 14.863 | 0 | 0.433 | 0.564 |
| UT | =~ | ut12 | 0.694 | 0.026 | 27.020 | 0 | 0.643 | 0.744 |
| FA | =~ | fa01 | 0.832 | 0.019 | 42.754 | 0 | 0.794 | 0.871 |
| FA | =~ | fa02 | 0.323 | 0.044 | 7.419 | 0 | 0.237 | 0.408 |
| FA | =~ | fa04 | 0.364 | 0.042 | 8.629 | 0 | 0.281 | 0.447 |
| FA | =~ | fa05 | 0.847 | 0.019 | 45.113 | 0 | 0.810 | 0.883 |
| FA | =~ | fa06 | 0.625 | 0.031 | 20.189 | 0 | 0.565 | 0.686 |
| FA | =~ | fa08 | 0.301 | 0.044 | 6.834 | 0 | 0.215 | 0.388 |
| FA | =~ | fa09 | 0.405 | 0.041 | 9.915 | 0 | 0.325 | 0.485 |
| FA | =~ | fa10 | 0.577 | 0.033 | 17.223 | 0 | 0.511 | 0.642 |
| DE | =~ | de01 | 0.581 | 0.033 | 17.839 | 0 | 0.517 | 0.645 |
| DE | =~ | de02 | 0.701 | 0.026 | 27.084 | 0 | 0.650 | 0.752 |
| DE | =~ | de03 | 0.613 | 0.031 | 19.820 | 0 | 0.552 | 0.673 |
| DE | =~ | de05 | 0.515 | 0.036 | 14.381 | 0 | 0.444 | 0.585 |
| DE | =~ | de06 | 0.544 | 0.033 | 16.735 | 0 | 0.480 | 0.608 |
| DE | =~ | de07 | 0.610 | 0.031 | 19.650 | 0 | 0.549 | 0.671 |
| DE | =~ | de08 | 0.691 | 0.026 | 26.096 | 0 | 0.639 | 0.743 |
| DE | =~ | de09 | 0.265 | 0.045 | 5.945 | 0 | 0.177 | 0.352 |
| DE | =~ | de10 | 0.711 | 0.025 | 28.077 | 0 | 0.661 | 0.760 |
| UN | =~ | un01 | 0.742 | 0.022 | 33.778 | 0 | 0.699 | 0.785 |
| UN | =~ | un02 | 0.831 | 0.016 | 52.516 | 0 | 0.800 | 0.862 |
| UN | =~ | un03 | 0.541 | 0.033 | 16.194 | 0 | 0.476 | 0.607 |
| UN | =~ | un04 | 0.735 | 0.022 | 32.767 | 0 | 0.691 | 0.779 |
| UN | =~ | un05 | 0.807 | 0.018 | 46.042 | 0 | 0.773 | 0.842 |
| UN | =~ | un06 | 0.493 | 0.036 | 13.845 | 0 | 0.423 | 0.563 |
| UN | =~ | un07 | 0.686 | 0.025 | 26.957 | 0 | 0.637 | 0.736 |
| UN | =~ | un08 | 0.753 | 0.021 | 35.455 | 0 | 0.711 | 0.795 |
| UN | =~ | un09 | 0.689 | 0.025 | 27.199 | 0 | 0.639 | 0.738 |
| UN | =~ | un10 | 0.709 | 0.024 | 29.489 | 0 | 0.662 | 0.756 |
| UN | =~ | un11 | 0.771 | 0.020 | 38.552 | 0 | 0.732 | 0.810 |
| UN | =~ | un12 | 0.736 | 0.022 | 32.952 | 0 | 0.692 | 0.780 |
| DT | =~ | PR | 0.949 | 0.015 | 64.540 | 0 | 0.921 | 0.978 |
| DT | =~ | CO | 0.710 | 0.029 | 24.513 | 0 | 0.653 | 0.767 |
| DT | =~ | UT | 0.749 | 0.025 | 29.589 | 0 | 0.699 | 0.798 |
| DT | =~ | FA | 0.672 | 0.031 | 21.357 | 0 | 0.610 | 0.733 |
| DT | =~ | DE | 0.934 | 0.016 | 59.214 | 0 | 0.903 | 0.965 |
| DT | =~ | UN | 0.393 | 0.042 | 9.300 | 0 | 0.310 | 0.476 |
Covariances:
smodel4 %>%
filter(op == "~~" & lhs != rhs) %>%
kable(
col.names = c(
"Factor",
"",
"Factor",
"Covariance",
"SE",
"z",
"p",
"CI lower bound",
"CI upper bound"
),
digits = 3
)| Factor | Factor | Covariance | SE | z | p | CI lower bound | CI upper bound | |
|---|---|---|---|---|---|---|---|---|
| de05 | ~~ | de09 | 0.609 | 0.029 | 21.091 | 0 | 0.553 | 0.666 |
| pr05 | ~~ | de06 | 0.606 | 0.030 | 20.237 | 0 | 0.547 | 0.664 |
| fa02 | ~~ | fa08 | 0.534 | 0.032 | 16.465 | 0 | 0.471 | 0.598 |
| pr05 | ~~ | ut11 | 0.543 | 0.033 | 16.451 | 0 | 0.478 | 0.608 |
| fa02 | ~~ | fa09 | 0.508 | 0.034 | 15.001 | 0 | 0.442 | 0.575 |
| fa08 | ~~ | fa09 | 0.482 | 0.035 | 13.759 | 0 | 0.413 | 0.551 |
| ut11 | ~~ | de06 | 0.499 | 0.035 | 14.067 | 0 | 0.429 | 0.568 |
Residuals:
smodel4 %>%
filter(op == "~~" & lhs == rhs) %>%
select(-(2:3)) %>%
kable(
col.names = c(
"Item",
"Residual",
"SE",
"z",
"p",
"CI lower bound",
"CI upper bound"
),
digits = 3
)| Item | Residual | SE | z | p | CI lower bound | CI upper bound |
|---|---|---|---|---|---|---|
| pr01 | 0.384 | 0.032 | 12.040 | 0 | 0.321 | 0.446 |
| pr02 | 0.662 | 0.038 | 17.637 | 0 | 0.589 | 0.736 |
| pr05 | 0.768 | 0.033 | 23.137 | 0 | 0.703 | 0.834 |
| pr06 | 0.656 | 0.038 | 17.466 | 0 | 0.582 | 0.730 |
| pr07 | 0.461 | 0.035 | 13.339 | 0 | 0.393 | 0.529 |
| pr08 | 0.463 | 0.035 | 13.380 | 0 | 0.396 | 0.531 |
| pr09 | 0.649 | 0.038 | 17.260 | 0 | 0.575 | 0.722 |
| pr10 | 0.751 | 0.036 | 20.758 | 0 | 0.680 | 0.821 |
| co01 | 0.453 | 0.036 | 12.702 | 0 | 0.383 | 0.523 |
| co02 | 0.534 | 0.038 | 14.214 | 0 | 0.460 | 0.607 |
| co03 | 0.742 | 0.037 | 20.020 | 0 | 0.670 | 0.815 |
| co04 | 0.879 | 0.030 | 29.669 | 0 | 0.821 | 0.937 |
| co05 | 0.395 | 0.034 | 11.658 | 0 | 0.328 | 0.461 |
| co06 | 0.619 | 0.038 | 16.102 | 0 | 0.544 | 0.695 |
| co08 | 0.889 | 0.029 | 31.020 | 0 | 0.833 | 0.945 |
| co09 | 0.474 | 0.036 | 13.089 | 0 | 0.403 | 0.545 |
| co10 | 0.634 | 0.038 | 16.468 | 0 | 0.558 | 0.709 |
| ut01 | 0.399 | 0.032 | 12.580 | 0 | 0.337 | 0.462 |
| ut02 | 0.309 | 0.028 | 11.121 | 0 | 0.254 | 0.363 |
| ut03 | 0.759 | 0.036 | 21.293 | 0 | 0.689 | 0.828 |
| ut04 | 0.783 | 0.035 | 22.515 | 0 | 0.715 | 0.851 |
| ut05 | 0.576 | 0.037 | 15.700 | 0 | 0.504 | 0.648 |
| ut06 | 0.440 | 0.033 | 13.230 | 0 | 0.375 | 0.505 |
| ut07 | 0.672 | 0.037 | 18.087 | 0 | 0.599 | 0.744 |
| ut08 | 0.608 | 0.037 | 16.418 | 0 | 0.536 | 0.681 |
| ut09 | 0.566 | 0.037 | 15.496 | 0 | 0.495 | 0.638 |
| ut11 | 0.751 | 0.033 | 22.463 | 0 | 0.686 | 0.817 |
| ut12 | 0.519 | 0.036 | 14.579 | 0 | 0.449 | 0.589 |
| fa01 | 0.307 | 0.032 | 9.478 | 0 | 0.244 | 0.371 |
| fa02 | 0.896 | 0.028 | 31.898 | 0 | 0.841 | 0.951 |
| fa04 | 0.867 | 0.031 | 28.217 | 0 | 0.807 | 0.928 |
| fa05 | 0.283 | 0.032 | 8.919 | 0 | 0.221 | 0.346 |
| fa06 | 0.609 | 0.039 | 15.727 | 0 | 0.533 | 0.685 |
| fa08 | 0.909 | 0.027 | 34.200 | 0 | 0.857 | 0.961 |
| fa09 | 0.836 | 0.033 | 25.281 | 0 | 0.771 | 0.901 |
| fa10 | 0.668 | 0.039 | 17.290 | 0 | 0.592 | 0.743 |
| de01 | 0.662 | 0.038 | 17.495 | 0 | 0.588 | 0.737 |
| de02 | 0.508 | 0.036 | 13.997 | 0 | 0.437 | 0.579 |
| de03 | 0.624 | 0.038 | 16.480 | 0 | 0.550 | 0.699 |
| de05 | 0.735 | 0.037 | 19.964 | 0 | 0.663 | 0.807 |
| de06 | 0.704 | 0.035 | 19.883 | 0 | 0.634 | 0.773 |
| de07 | 0.628 | 0.038 | 16.559 | 0 | 0.553 | 0.702 |
| de08 | 0.522 | 0.037 | 14.260 | 0 | 0.450 | 0.594 |
| de09 | 0.930 | 0.024 | 39.497 | 0 | 0.884 | 0.976 |
| de10 | 0.495 | 0.036 | 13.747 | 0 | 0.424 | 0.565 |
| un01 | 0.450 | 0.033 | 13.798 | 0 | 0.386 | 0.514 |
| un02 | 0.309 | 0.026 | 11.737 | 0 | 0.257 | 0.361 |
| un03 | 0.707 | 0.036 | 19.564 | 0 | 0.636 | 0.778 |
| un04 | 0.460 | 0.033 | 13.962 | 0 | 0.396 | 0.525 |
| un05 | 0.348 | 0.028 | 12.310 | 0 | 0.293 | 0.404 |
| un06 | 0.757 | 0.035 | 21.539 | 0 | 0.688 | 0.826 |
| un07 | 0.529 | 0.035 | 15.126 | 0 | 0.460 | 0.597 |
| un08 | 0.433 | 0.032 | 13.545 | 0 | 0.370 | 0.496 |
| un09 | 0.526 | 0.035 | 15.068 | 0 | 0.457 | 0.594 |
| un10 | 0.497 | 0.034 | 14.565 | 0 | 0.430 | 0.564 |
| un11 | 0.405 | 0.031 | 13.128 | 0 | 0.345 | 0.466 |
| un12 | 0.458 | 0.033 | 13.931 | 0 | 0.394 | 0.523 |
| PR | 0.098 | 0.028 | 3.526 | 0 | 0.044 | 0.153 |
| CO | 0.496 | 0.041 | 12.038 | 0 | 0.415 | 0.576 |
| UT | 0.439 | 0.038 | 11.589 | 0 | 0.365 | 0.514 |
| FA | 0.549 | 0.042 | 13.001 | 0 | 0.466 | 0.632 |
| DE | 0.127 | 0.029 | 4.312 | 0 | 0.069 | 0.185 |
| UN | 0.846 | 0.033 | 25.464 | 0 | 0.781 | 0.911 |
| DT | 1.000 | 0.000 | NA | NA | 1.000 | 1.000 |
semPaths(model4,
what = "std",
whatLabels = "est",
style = "lisrel",
residScale = 10,
theme = "colorblind",
rotation = 1,
layout = "tree",
cardinal = "lat cov",
curvePivot = TRUE,
sizeMan = 3,
sizeLat = 7)taia %>%
select(id, all_of(taia_items_2)) %>%
pivot_longer(all_of(taia_items_2),
names_to = "subscale",
values_to = "score") %>%
mutate(subscale = str_remove_all(subscale, "[:digit:]{2}") %>% toupper()) %>%
group_by(id, subscale) %>%
summarise(total_score = sum(score)) %>%
ungroup() %>%
pivot_wider(id_cols = id,
names_from = subscale,
values_from = total_score) %>%
relocate(after = c(id, PR, CO, UT, FA, DE, UN)) %>%
mutate(DT = PR + CO + UT + FA + DE + UN) %>%
full_join(taia) -> taia## `summarise()` regrouping output by 'id' (override with `.groups` argument)
## Joining, by = "id"
taia %>%
pivot_longer(cols = c("PR", "CO", "UT", "FA", "DE", "UN"),
names_to = "subscale",
values_to = "score") %>%
mutate(subscale = factor(subscale, levels = c("PR", "CO", "UT", "FA", "DE", "UN"))) -> taia_ltaia_l %>%
ggplot(aes(score, gt_score, color = subscale)) +
geom_point(alpha = .3) +
geom_smooth(method = "lm") +
facet_wrap(~ subscale, scales = "free") +
scale_color_manual(values = clrs) +
guides(color = FALSE) +
labs(x = "TAIA subscale total score",
y = "General Trust Scale total score",
title = "Corelations between General Trust and TAIA subscales") +
theme(plot.title = element_text(hjust = .5))## `geom_smooth()` using formula 'y ~ x'
##
## Pearson's product-moment correlation
##
## data: taia$PR and taia$gt_score
## t = 3.512, df = 493, p-value = 0.0004856
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.0690492 0.2410450
## sample estimates:
## cor
## 0.1562312
##
## Pearson's product-moment correlation
##
## data: taia$CO and taia$gt_score
## t = 3.842, df = 493, p-value = 0.000138
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.08362567 0.25480562
## sample estimates:
## cor
## 0.1705018
##
## Pearson's product-moment correlation
##
## data: taia$UT and taia$gt_score
## t = 2.4679, df = 493, p-value = 0.01393
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.02255584 0.19668679
## sample estimates:
## cor
## 0.110469
##
## Pearson's product-moment correlation
##
## data: taia$FA and taia$gt_score
## t = 1.6968, df = 493, p-value = 0.09036
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.01201538 0.16323459
## sample estimates:
## cor
## 0.07619805
##
## Pearson's product-moment correlation
##
## data: taia$DE and taia$gt_score
## t = 4.3405, df = 493, p-value = 1.726e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1055064 0.2753324
## sample estimates:
## cor
## 0.1918551
##
## Pearson's product-moment correlation
##
## data: taia$UN and taia$gt_score
## t = 3.5643, df = 493, p-value = 0.0004003
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.07136383 0.24323469
## sample estimates:
## cor
## 0.1584997
taia %>%
ggplot(aes(DT, gt_score)) +
geom_point(alpha = .3) +
geom_smooth(method = "lm", color = "black") +
labs(x = "TAIA score", y = "General Trust Score",
title = "Correlation between General Trust and TAIA") +
theme(plot.title = element_text(hjust = .5))## `geom_smooth()` using formula 'y ~ x'
##
## Pearson's product-moment correlation
##
## data: taia$DT and taia$gt_score
## t = 4.6048, df = 493, p-value = 5.261e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1170296 0.2860807
## sample estimates:
## cor
## 0.2030679
taia_l %>%
ggplot(aes(score, n_dighelp, color = subscale)) +
geom_point(alpha = .3) +
geom_smooth(method = "lm") +
facet_wrap(~ subscale) +
guides(color = FALSE) +
scale_color_manual(values = clrs) +
labs(x = "TAIA subscales total score",
y = "Number of digital helpers",
title = "Correlation TAIA subscales with number of digital helpers") +
theme(plot.title = element_text(hjust = .5))## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 948 rows containing non-finite values (stat_smooth).
## Warning: Removed 948 rows containing missing values (geom_point).
##
## Pearson's product-moment correlation
##
## data: taia$PR and taia$n_dighelp
## t = 1.1715, df = 335, p-value = 0.2422
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.04325762 0.16955094
## sample estimates:
## cor
## 0.06387275
##
## Pearson's product-moment correlation
##
## data: taia$CO and taia$n_dighelp
## t = -0.96471, df = 335, p-value = 0.3354
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.15857803 0.05450713
## sample estimates:
## cor
## -0.05263456
##
## Pearson's product-moment correlation
##
## data: taia$UT and taia$n_dighelp
## t = 1.3367, df = 335, p-value = 0.1822
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.03426592 0.17828379
## sample estimates:
## cor
## 0.0728359
##
## Pearson's product-moment correlation
##
## data: taia$FA and taia$n_dighelp
## t = -0.70154, df = 335, p-value = 0.4835
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1445447 0.0688158
## sample estimates:
## cor
## -0.03830097
##
## Pearson's product-moment correlation
##
## data: taia$DE and taia$n_dighelp
## t = 1.1408, df = 335, p-value = 0.2548
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.04492669 0.16792625
## sample estimates:
## cor
## 0.06220708
##
## Pearson's product-moment correlation
##
## data: taia$UN and taia$n_dighelp
## t = 1.4962, df = 335, p-value = 0.1355
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.02558116 0.18668686
## sample estimates:
## cor
## 0.0814767
##
## Pearson's product-moment correlation
##
## data: taia$DT and taia$n_dighelp
## t = 1.0065, df = 335, p-value = 0.3149
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.05223317 0.16080034
## sample estimates:
## cor
## 0.05490843
taia_l %>%
ggplot(aes(score, e_dighelp, color = subscale)) +
geom_point(alpha = .3) +
geom_smooth(method = "lm") +
facet_wrap(~ subscale) +
guides(color = FALSE) +
scale_color_manual(values = clrs) +
labs(x = "TAIA subscales total score",
y = "Estimate of dealing with digital helpers experience",
title = "Correlation TAIA subscales with expirience of dealing with digital helpers") +
theme(plot.title = element_text(hjust = .5))## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 948 rows containing non-finite values (stat_smooth).
## Warning: Removed 948 rows containing missing values (geom_point).
##
## Pearson's product-moment correlation
##
## data: taia$PR and taia$e_dighelp
## t = 6.4594, df = 335, p-value = 3.701e-10
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2342924 0.4245384
## sample estimates:
## cor
## 0.3327975
##
## Pearson's product-moment correlation
##
## data: taia$CO and taia$e_dighelp
## t = 4.0996, df = 335, p-value = 5.199e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1144032 0.3179773
## sample estimates:
## cor
## 0.218567
##
## Pearson's product-moment correlation
##
## data: taia$UT and taia$e_dighelp
## t = 5.5088, df = 335, p-value = 7.21e-08
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1871349 0.3832429
## sample estimates:
## cor
## 0.288208
##
## Pearson's product-moment correlation
##
## data: taia$FA and taia$e_dighelp
## t = 3.9708, df = 335, p-value = 8.772e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1076158 0.3117865
## sample estimates:
## cor
## 0.2120134
##
## Pearson's product-moment correlation
##
## data: taia$DE and taia$e_dighelp
## t = 5.5689, df = 335, p-value = 5.265e-08
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1901671 0.3859219
## sample estimates:
## cor
## 0.2910883
##
## Pearson's product-moment correlation
##
## data: taia$UN and taia$e_dighelp
## t = 1.7091, df = 335, p-value = 0.08836
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.01400204 0.19784231
## sample estimates:
## cor
## 0.09297223
##
## Pearson's product-moment correlation
##
## data: taia$DT and taia$e_dighelp
## t = 6.3515, df = 335, p-value = 6.95e-10
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2290252 0.4199649
## sample estimates:
## cor
## 0.3278389
taia_l %>%
ggplot(aes(score, n_socnet, color = subscale)) +
geom_point(alpha = .3) +
geom_smooth(method = "lm") +
facet_wrap(~ subscale) +
guides(color = FALSE) +
scale_color_manual(values = clrs) +
labs(x = "TAIA subscales total score",
y = "Number of social networks and social media",
title = "Correlation TAIA subscales with number of social networks and social media") +
theme(plot.title = element_text(hjust = .5))## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 360 rows containing non-finite values (stat_smooth).
## Warning: Removed 360 rows containing missing values (geom_point).
##
## Pearson's product-moment correlation
##
## data: taia$PR and taia$n_socnet
## t = 1.7563, df = 433, p-value = 0.07974
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.009995365 0.176726793
## sample estimates:
## cor
## 0.08410396
##
## Pearson's product-moment correlation
##
## data: taia$CO and taia$n_socnet
## t = -0.18106, df = 433, p-value = 0.8564
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.10263724 0.08538923
## sample estimates:
## cor
## -0.008700912
##
## Pearson's product-moment correlation
##
## data: taia$UT and taia$n_socnet
## t = 3.3825, df = 433, p-value = 0.0007836
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.06744242 0.25068403
## sample estimates:
## cor
## 0.1604453
##
## Pearson's product-moment correlation
##
## data: taia$FA and taia$n_socnet
## t = 2.2881, df = 433, p-value = 0.02261
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.01543692 0.20125076
## sample estimates:
## cor
## 0.1092986
##
## Pearson's product-moment correlation
##
## data: taia$DE and taia$n_socnet
## t = 3.6857, df = 433, p-value = 0.000257
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.08172823 0.26409798
## sample estimates:
## cor
## 0.1744083
##
## Pearson's product-moment correlation
##
## data: taia$UN and taia$n_socnet
## t = 2.8836, df = 433, p-value = 0.004128
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.04380848 0.22833688
## sample estimates:
## cor
## 0.1372634
##
## Pearson's product-moment correlation
##
## data: taia$DT and taia$n_socnet
## t = 3.3815, df = 433, p-value = 0.0007863
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.06739663 0.25064092
## sample estimates:
## cor
## 0.1604005
taia_l %>%
ggplot(aes(score, f_socnet, color = subscale)) +
geom_point(alpha = .3) +
geom_smooth(method = "lm") +
facet_wrap(~ subscale) +
guides(color = FALSE) +
scale_color_manual(values = clrs) +
labs(x = "TAIA subscales total score",
y = "Frequency of social networks and social media use",
title = "Correlation TAIA subscales with frequency of social networks and social media use") +
theme(plot.title = element_text(hjust = .5))## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 360 rows containing non-finite values (stat_smooth).
## Warning: Removed 360 rows containing missing values (geom_point).
##
## Pearson's product-moment correlation
##
## data: taia$PR and taia$f_socnet
## t = 1.1135, df = 433, p-value = 0.2661
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.04078842 0.14671995
## sample estimates:
## cor
## 0.0534368
##
## Pearson's product-moment correlation
##
## data: taia$CO and taia$f_socnet
## t = 0.83383, df = 433, p-value = 0.4048
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.05418487 0.13355691
## sample estimates:
## cor
## 0.0400394
##
## Pearson's product-moment correlation
##
## data: taia$UT and taia$f_socnet
## t = -0.18344, df = 433, p-value = 0.8545
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.10275052 0.08527558
## sample estimates:
## cor
## -0.008815392
##
## Pearson's product-moment correlation
##
## data: taia$FA and taia$f_socnet
## t = 0.096068, df = 433, p-value = 0.9235
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.08944245 0.09859416
## sample estimates:
## cor
## 0.004616666
##
## Pearson's product-moment correlation
##
## data: taia$DE and taia$f_socnet
## t = 0.53504, df = 433, p-value = 0.5929
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.06848192 0.11943552
## sample estimates:
## cor
## 0.02570387
##
## Pearson's product-moment correlation
##
## data: taia$UN and taia$f_socnet
## t = -0.29625, df = 433, p-value = 0.7672
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.10811106 0.07989175
## sample estimates:
## cor
## -0.01423547
##
## Pearson's product-moment correlation
##
## data: taia$DT and taia$f_socnet
## t = 0.36218, df = 433, p-value = 0.7174
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.07674342 0.11124072
## sample estimates:
## cor
## 0.01740244
taia_l %>%
ggplot(aes(score, e_socnet, color = subscale)) +
geom_point(alpha = .3) +
geom_smooth(method = "lm") +
facet_wrap(~ subscale) +
guides(color = FALSE) +
scale_color_manual(values = clrs) +
labs(x = "TAIA subscales total score",
y = "Estimate of dealing with recommender systems experience",
title = "Correlation TAIA subscales with experience of dealing with recommender systems") +
theme(plot.title = element_text(hjust = .5))## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 360 rows containing non-finite values (stat_smooth).
## Warning: Removed 360 rows containing missing values (geom_point).
##
## Pearson's product-moment correlation
##
## data: taia$PR and taia$e_socnet
## t = 4.6159, df = 433, p-value = 5.164e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1250906 0.3043865
## sample estimates:
## cor
## 0.2165639
##
## Pearson's product-moment correlation
##
## data: taia$CO and taia$e_socnet
## t = 5.3414, df = 433, p-value = 1.493e-07
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1583093 0.3348224
## sample estimates:
## cor
## 0.2486289
##
## Pearson's product-moment correlation
##
## data: taia$UT and taia$e_socnet
## t = 4.0939, df = 433, p-value = 5.061e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1008503 0.2819433
## sample estimates:
## cor
## 0.1930402
##
## Pearson's product-moment correlation
##
## data: taia$FA and taia$e_socnet
## t = 3.2486, df = 433, p-value = 0.00125
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.06111661 0.24472172
## sample estimates:
## cor
## 0.1542505
##
## Pearson's product-moment correlation
##
## data: taia$DE and taia$e_socnet
## t = 5.5098, df = 433, p-value = 6.177e-08
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1659355 0.3417581
## sample estimates:
## cor
## 0.2559625
##
## Pearson's product-moment correlation
##
## data: taia$UN and taia$e_socnet
## t = 1.7866, df = 433, p-value = 0.0747
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.008545219 0.178131408
## sample estimates:
## cor
## 0.0855438
##
## Pearson's product-moment correlation
##
## data: taia$DT and taia$e_socnet
## t = 5.4871, df = 433, p-value = 6.965e-08
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1649116 0.3408280
## sample estimates:
## cor
## 0.2549784
taia %>%
pivot_longer(cols = c("PR", "CO", "UT", "FA", "DE", "UN"),
names_to = "subscale",
values_to = "score") %>%
mutate(subscale = factor(subscale, levels = c("PR", "CO", "UT", "FA", "DE", "UN"))) %>%
ggplot(aes(score, selfdrexp, color = subscale)) +
geom_point(alpha = .3) +
geom_smooth(method = "lm") +
facet_wrap(~ subscale) +
guides(color = FALSE) +
scale_color_manual(values = clrs) +
labs(x = "TAIA subscales total score",
y = "Estimate of selfdriving car experience",
title = "Correlation TAIA subscales with selfdriving car experience") +
theme(plot.title = element_text(hjust = .5))## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 2892 rows containing non-finite values (stat_smooth).
## Warning: Removed 2892 rows containing missing values (geom_point).
##
## Pearson's product-moment correlation
##
## data: taia$PR and taia$selfdrexp
## t = 0.84968, df = 11, p-value = 0.4136
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3507759 0.7030276
## sample estimates:
## cor
## 0.2481747
##
## Pearson's product-moment correlation
##
## data: taia$CO and taia$selfdrexp
## t = -0.35361, df = 11, p-value = 0.7303
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.6207428 0.4725725
## sample estimates:
## cor
## -0.1060178
##
## Pearson's product-moment correlation
##
## data: taia$UT and taia$selfdrexp
## t = 0.99003, df = 11, p-value = 0.3434
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3145201 0.7230638
## sample estimates:
## cor
## 0.2860336
##
## Pearson's product-moment correlation
##
## data: taia$FA and taia$selfdrexp
## t = -0.49537, df = 11, p-value = 0.6301
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.6461169 0.4389957
## sample estimates:
## cor
## -0.1477201
##
## Pearson's product-moment correlation
##
## data: taia$DE and taia$selfdrexp
## t = -0.3201, df = 11, p-value = 0.7549
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.6145242 0.4803441
## sample estimates:
## cor
## -0.09606636
##
## Pearson's product-moment correlation
##
## data: taia$UN and taia$selfdrexp
## t = -0.11887, df = 11, p-value = 0.9075
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.5754466 0.5255391
## sample estimates:
## cor
## -0.03581772
##
## Pearson's product-moment correlation
##
## data: taia$DT and taia$selfdrexp
## t = 0.17179, df = 11, p-value = 0.8667
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.5139042 0.5860113
## sample estimates:
## cor
## 0.05172814
taia %>%
pivot_longer(cols = c("PR", "CO", "UT", "FA", "DE", "UN"),
names_to = "subscale",
values_to = "score") %>%
mutate(subscale = factor(subscale, levels = c("PR", "CO", "UT", "FA", "DE", "UN"))) %>%
ggplot(aes(score, selfdrsafe, color = subscale)) +
geom_point(alpha = .3) +
geom_smooth(method = "lm") +
facet_wrap(~ subscale) +
guides(color = FALSE) +
scale_color_manual(values = clrs) +
labs(x = "TAIA subscales total score",
y = "Estimate of selfdriving car safety",
title = "Correlation TAIA subscales with selfdriving car safety") +
theme(plot.title = element_text(hjust = .5))## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 2892 rows containing non-finite values (stat_smooth).
## Warning: Removed 2892 rows containing missing values (geom_point).
##
## Pearson's product-moment correlation
##
## data: taia$PR and taia$selfdrsafe
## t = -0.13243, df = 11, p-value = 0.897
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.5781736 0.5225747
## sample estimates:
## cor
## -0.03989866
##
## Pearson's product-moment correlation
##
## data: taia$CO and taia$selfdrsafe
## t = 0.31876, df = 11, p-value = 0.7559
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.4806533 0.6142740
## sample estimates:
## cor
## 0.09566808
##
## Pearson's product-moment correlation
##
## data: taia$UT and taia$selfdrsafe
## t = -0.15671, df = 11, p-value = 0.8783
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.5830211 0.5172388
## sample estimates:
## cor
## -0.04719733
##
## Pearson's product-moment correlation
##
## data: taia$FA and taia$selfdrsafe
## t = 0.98457, df = 11, p-value = 0.346
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3159418 0.7223097
## sample estimates:
## cor
## 0.2845836
##
## Pearson's product-moment correlation
##
## data: taia$DE and taia$selfdrsafe
## t = -0.91642, df = 11, p-value = 0.3791
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.7127275 0.3336096
## sample estimates:
## cor
## -0.2663311
##
## Pearson's product-moment correlation
##
## data: taia$UN and taia$selfdrsafe
## t = -1.0936, df = 11, p-value = 0.2975
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.7369795 0.2874227
## sample estimates:
## cor
## -0.3131557
##
## Pearson's product-moment correlation
##
## data: taia$DT and taia$selfdrsafe
## t = -0.34044, df = 11, p-value = 0.7399
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.6183078 0.4756360
## sample estimates:
## cor
## -0.1021088
taia %>%
pivot_longer(cols = c("PR", "CO", "UT", "FA", "DE", "UN"),
names_to = "subscale",
values_to = "score") %>%
mutate(subscale = factor(subscale, levels = c("PR", "CO", "UT", "FA", "DE", "UN"))) %>%
ggplot(aes(score, eduaiexp, color = subscale)) +
geom_point(alpha = .3) +
geom_smooth(method = "lm") +
facet_wrap(~ subscale) +
guides(color = FALSE) +
scale_color_manual(values = clrs) +
labs(x = "TAIA subscales total score",
y = "Estimate of dealing with education AI experience",
title = "Correlation TAIA subscales with experience of dealing with education AI") +
theme(plot.title = element_text(hjust = .5))## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 2298 rows containing non-finite values (stat_smooth).
## Warning: Removed 2298 rows containing missing values (geom_point).
##
## Pearson's product-moment correlation
##
## data: taia$PR and taia$eduaiexp
## t = 5.7393, df = 110, p-value = 8.512e-08
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.3232882 0.6111631
## sample estimates:
## cor
## 0.480047
##
## Pearson's product-moment correlation
##
## data: taia$CO and taia$eduaiexp
## t = 4.8673, df = 110, p-value = 3.807e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2553469 0.5625701
## sample estimates:
## cor
## 0.4209573
##
## Pearson's product-moment correlation
##
## data: taia$UT and taia$eduaiexp
## t = 4.1902, df = 110, p-value = 5.648e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1991635 0.5207166
## sample estimates:
## cor
## 0.3710084
##
## Pearson's product-moment correlation
##
## data: taia$FA and taia$eduaiexp
## t = 3.4196, df = 110, p-value = 0.0008808
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1320205 0.4685858
## sample estimates:
## cor
## 0.3099826
##
## Pearson's product-moment correlation
##
## data: taia$DE and taia$eduaiexp
## t = 6.8158, df = 110, p-value = 5.253e-10
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.3997687 0.6633847
## sample estimates:
## cor
## 0.5449038
##
## Pearson's product-moment correlation
##
## data: taia$UN and taia$eduaiexp
## t = 3.3073, df = 110, p-value = 0.001273
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1219943 0.4605948
## sample estimates:
## cor
## 0.3007422
##
## Pearson's product-moment correlation
##
## data: taia$DT and taia$eduaiexp
## t = 6.6377, df = 110, p-value = 1.249e-09
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.3876975 0.6553096
## sample estimates:
## cor
## 0.5347814
##
## f m
## 233 262
##
## f m
## 0.47 0.53
##
## 2-sample test for equality of proportions with continuity correction
##
## data: c(233, 0.54 * 495) out of c(495, 495)
## X-squared = 4.4809, df = 1, p-value = 0.03428
## alternative hypothesis: two.sided
## 95 percent confidence interval:
## -0.133451569 -0.005134289
## sample estimates:
## prop 1 prop 2
## 0.4707071 0.5400000
##
## 2-sample test for equality of proportions with continuity correction
##
## data: c(262, 0.46 * 495) out of c(495, 495)
## X-squared = 4.4809, df = 1, p-value = 0.03428
## alternative hypothesis: two.sided
## 95 percent confidence interval:
## 0.005134289 0.133451569
## sample estimates:
## prop 1 prop 2
## 0.5292929 0.4600000
taia %>%
ggplot(aes(age)) +
geom_histogram(binwidth = 1, fill = "black") +
facet_grid(sex ~ .,
labeller = labeller(sex = c(f = "Females", m = "Males"))) +
scale_x_continuous(breaks = seq(20, 80, 5)) +
labs(x = "Age", y = "Count",
title = "Age distributions by gender")taia %>%
group_by(sex) %>%
summarise(min = min(age),
max = max(age),
median = median(age),
mean = mean(age))## `summarise()` ungrouping output (override with `.groups` argument)
## # A tibble: 2 x 5
## sex min max median mean
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 f 17 81 34 35.5
## 2 m 18 61 35.5 36.1
##
## Welch Two Sample t-test
##
## data: taia$age by taia$sex
## t = -0.69515, df = 472.19, p-value = 0.4873
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -2.545920 1.215316
## sample estimates:
## mean in group f mean in group m
## 35.4721 36.1374
taia %>%
mutate(city = tolower(city)) %>%
mutate(city = recode(city,
"м" = "москва",
"мск" = "москва",
"saint-petersburg" = "санкт-петербург",
"санкт петербург" = "санкт-петербург",
"рф, санкт-петербург" = "санкт-петербург",
"спб" = "санкт-петербург")) %>%
group_by(city) %>%
summarise(n = n()) %>%
arrange(desc(n)) %>%
kable(col.names = c("City", "Num. of subjects"))## `summarise()` ungrouping output (override with `.groups` argument)
| City | Num. of subjects |
|---|---|
| москва | 81 |
| санкт-петербург | 28 |
| екатеринбург | 12 |
| краснодар | 12 |
| казань | 11 |
| ростов-на-дону | 11 |
| самара | 11 |
| новосибирск | 10 |
| саратов | 10 |
| уфа | 10 |
| пермь | 9 |
| воронеж | 8 |
| пенза | 7 |
| челябинск | 7 |
| иркутск | 5 |
| калининград | 5 |
| красноярск | 5 |
| нижний новгород | 5 |
| саранск | 5 |
| ярославль | 5 |
| иваново | 4 |
| кемерово | 4 |
| кострома | 4 |
| омск | 4 |
| смоленск | 4 |
| томск | 4 |
| чебоксары | 4 |
| барнаул | 3 |
| брянск | 3 |
| владимир | 3 |
| волгоград | 3 |
| ижевск | 3 |
| йошкар-ола | 3 |
| киров | 3 |
| оренбург | 3 |
| петушки | 3 |
| таганрог | 3 |
| тамбов | 3 |
| тольятти | 3 |
| энгельс | 3 |
| архангельск | 2 |
| астрахань | 2 |
| белгород | 2 |
| бийск | 2 |
| братск | 2 |
| великий новгород | 2 |
| владивосток | 2 |
| ишим | 2 |
| красногорск | 2 |
| кропоткин | 2 |
| кузнецк | 2 |
| курск | 2 |
| магнитогорск | 2 |
| майкоп | 2 |
| мурманск | 2 |
| подольск | 2 |
| псков | 2 |
| рыбинск | 2 |
| рязань | 2 |
| сафоново | 2 |
| севастополь | 2 |
| стерлитамак | 2 |
| тула | 2 |
| тюмень | 2 |
| череповец | 2 |
| чита | 2 |
| шахты | 2 |
| - | 1 |
| 1987 | 1 |
| абакан | 1 |
| азов | 1 |
| алтай | 1 |
| анапа | 1 |
| анжеро-судженск | 1 |
| армавир | 1 |
| афанасьево | 1 |
| балашиха | 1 |
| белово | 1 |
| белореченск | 1 |
| березники | 1 |
| благовещенск | 1 |
| бронницы | 1 |
| бугульма | 1 |
| волгодонск | 1 |
| вологда | 1 |
| воркута | 1 |
| воткинск | 1 |
| всеволожск, ленинградская область | 1 |
| выборг | 1 |
| вязьма | 1 |
| г. миасс | 1 |
| геленджик | 1 |
| гусев | 1 |
| далматово | 1 |
| дербент | 1 |
| джанкой | 1 |
| дзержинск | 1 |
| дивногорск | 1 |
| добрянка | 1 |
| донецк | 1 |
| донецк рф | 1 |
| дубна | 1 |
| железногорск курская область | 1 |
| жигулевск | 1 |
| жуковский | 1 |
| зверево | 1 |
| зеленоград | 1 |
| златоуст | 1 |
| калтан | 1 |
| катайск | 1 |
| качуг | 1 |
| керчь | 1 |
| кизляр | 1 |
| кизнер | 1 |
| кинешма | 1 |
| киржач | 1 |
| кисловодск | 1 |
| кола | 1 |
| кондопога | 1 |
| королёв | 1 |
| красноусольский | 1 |
| красноуфимск | 1 |
| кривой рог | 1 |
| курган | 1 |
| луга | 1 |
| луховицы | 1 |
| мариинск | 1 |
| махачкала | 1 |
| минеральные воды | 1 |
| минск | 1 |
| мичуринск | 1 |
| набережные челны | 1 |
| нефтегорск | 1 |
| нижнекамск | 1 |
| новоалтайск. | 1 |
| новокузнецк | 1 |
| новокуйбышевск | 1 |
| новочебоксарск | 1 |
| няндома | 1 |
| орел | 1 |
| орехово-зуево | 1 |
| орловская область | 1 |
| пензенская область | 1 |
| первоуральск | 1 |
| пермский край, г.верещагино | 1 |
| покровское | 1 |
| полысаево | 1 |
| починок | 1 |
| прокопьевск | 1 |
| ревда | 1 |
| россия. ростовская область. город ростов-на-дону. | 1 |
| ростов | 1 |
| ростов на дону | 1 |
| ростов- на - дону | 1 |
| ряань | 1 |
| североморск | 1 |
| село тюбук | 1 |
| сибай | 1 |
| сіктівкар | 1 |
| славгород | 1 |
| сланцы | 1 |
| соликамск | 1 |
| сочи | 1 |
| ставрополь | 1 |
| старый оскол | 1 |
| суздаль | 1 |
| сургут | 1 |
| таганрог и макеевка | 1 |
| тайшет | 1 |
| татарстан | 1 |
| тверь | 1 |
| тобольск | 1 |
| ужур | 1 |
| ульяновск | 1 |
| уссурийск | 1 |
| усть-катав | 1 |
| хабаровск | 1 |
| чайковский | 1 |
| чапаевск | 1 |
| чкаловск | 1 |
| щёлково | 1 |
| электрогорск | 1 |
| электросталь | 1 |
| юрга | 1 |
taia %>%
mutate(spec1 = tolower(spec1)) %>%
group_by(spec1) %>%
summarise(n = n()) %>%
arrange(desc(n)) %>%
kable()## `summarise()` ungrouping output (override with `.groups` argument)
| spec1 | n |
|---|---|
| экономист | 26 |
| инженер | 23 |
| юриспруденция | 14 |
| юрист | 14 |
| бухгалтер | 12 |
| менеджмент | 10 |
| повар | 9 |
| психология | 9 |
| менеджер | 6 |
| экономика | 6 |
| нет | 5 |
| педагог | 5 |
| товаровед | 5 |
| менеджмент организации | 4 |
| программист | 4 |
| психолог | 4 |
| техник | 4 |
| технолог | 4 |
| швея | 4 |
| ветеринарный врач | 3 |
| геолог | 3 |
| инженер-конструктор | 3 |
| история | 3 |
| медсестра | 3 |
| механик | 3 |
| нет специальности | 3 |
| туризм | 3 |
| филолог | 3 |
| финансы и кредит | 3 |
| автомеханик | 2 |
| асу | 2 |
| биолог | 2 |
| бухгалтерия | 2 |
| водитель | 2 |
| геодезия | 2 |
| инженер механик | 2 |
| инженер-строитель | 2 |
| информатик-экономист | 2 |
| информатика и вычислительная техника | 2 |
| коррекционное образование | 2 |
| маркетолог | 2 |
| медик | 2 |
| оператор | 2 |
| оператор пк | 2 |
| перевод и переводоведение | 2 |
| программирование | 2 |
| радиотехник | 2 |
| сварщик | 2 |
| слесарь | 2 |
| строительство | 2 |
| технолог общественного питания | 2 |
| техносферная безопасность | 2 |
| товаровед-эксперт | 2 |
| торговля | 2 |
| учитель русского языка и литературы | 2 |
| филология | 2 |
| химия | 2 |
| эколог | 2 |
| экология | 2 |
| экономическое | 2 |
| электрик | 2 |
| электроэнергетика | 2 |
| - | 1 |
| (в школе) направление дизайна | 1 |
| 9 классов, сейчас заканчиваю учебу в колледже | 1 |
| pr | 1 |
| radio | 1 |
| schienze del turismo | 1 |
| авто механик | 1 |
| автомеханник | 1 |
| автомобилестроение | 1 |
| авторихтовщик | 1 |
| агросервис транспорта и энергетики | 1 |
| актёр театра драмы и кино | 1 |
| артист | 1 |
| архитектор | 1 |
| архитектор-реставратор | 1 |
| бакалавр журналистики | 1 |
| безопасность жизнедеятельности | 1 |
| бизнес-информатика | 1 |
| биологические науки | 1 |
| биология | 1 |
| бух.учет | 1 |
| бухгалтер-экономист | 1 |
| бухгалтерский учет | 1 |
| бухгалтерский учет и аудит | 1 |
| бухучет | 1 |
| верстальщик | 1 |
| военный | 1 |
| вокалист | 1 |
| гму | 1 |
| горный инженер | 1 |
| гос служба | 1 |
| гостиничное дело | 1 |
| гостиничный сервис | 1 |
| государственное и муниципальное управление | 1 |
| гримёр | 1 |
| гум | 1 |
| дизайн | 1 |
| документоведение и архивоведение | 1 |
| документоведение и информационная аналитика | 1 |
| журналист | 1 |
| журналистика | 1 |
| зооинженер | 1 |
| иллюстратор | 1 |
| инженер автомобилестроения | 1 |
| инженер асои | 1 |
| инженер водоснабжения | 1 |
| инженер горный | 1 |
| инженер информационных технологий и систем | 1 |
| инженер конструктор | 1 |
| инженер конструктор технолог эва | 1 |
| инженер микроэлектроники | 1 |
| инженер по земельному кадастру | 1 |
| инженер связи | 1 |
| инженер строитель | 1 |
| инженер технолог | 1 |
| инженер-механик | 1 |
| инженер-экономист лесной промышленности и лесного хозяйства | 1 |
| инженер-электрик | 1 |
| информатик- экономист | 1 |
| информатика и икт | 1 |
| информационная безопасность | 1 |
| информационные системы | 1 |
| информационные системы в экономике | 1 |
| информационные системы и технологии | 1 |
| информационные технологии и системы | 1 |
| историк-исследователь | 1 |
| каменщик | 1 |
| кибернетик | 1 |
| кибернетика | 1 |
| коммерсант | 1 |
| коммерция | 1 |
| компьютерная инженерия | 1 |
| компьютерные системы управления | 1 |
| кондитер | 1 |
| консервный мастер | 1 |
| конструктор-дизайнер | 1 |
| культурология | 1 |
| лаборант | 1 |
| лечебное дело | 1 |
| лингвист-переводчик | 1 |
| логопедия | 1 |
| маляр | 1 |
| маркетинг (только начал обучение | 1 |
| мастер отделочных работ | 1 |
| мастер сельхоз производства | 1 |
| математика | 1 |
| математический факультет нгпу (неоконченное) | 1 |
| материаловедение | 1 |
| материаловедение и технология материалов | 1 |
| машинист | 1 |
| машинист крана | 1 |
| машины и технологии литейного производства | 1 |
| медицинская сестра | 1 |
| менеджер по персоналу | 1 |
| менеджер по продажам | 1 |
| менеджмент спорта и туризма | 1 |
| металловедение и термическая обработка металлов | 1 |
| металлургия | 1 |
| монтаж, наладка и эксплуатация электрического оборудования гражданских и промышленных зданий | 1 |
| монтажник | 1 |
| монтёр аэс | 1 |
| не училась | 1 |
| никакой | 1 |
| обработка металлов давлением | 1 |
| общеобразовательная школп | 1 |
| омд | 1 |
| омс | 1 |
| оператор эвм | 1 |
| отсутствует | 1 |
| пед | 1 |
| педагог-электротехник | 1 |
| педагогическое | 1 |
| педиатрия | 1 |
| переводчик | 1 |
| повар кондитер | 1 |
| повар-технолог | 1 |
| повор | 1 |
| помощник машиниста | 1 |
| право | 1 |
| правовед | 1 |
| преподаватель | 1 |
| преподаватель английского языка | 1 |
| преподаватель начальных классов | 1 |
| преподаватель рус. яз., лит-ры и ин. языка | 1 |
| преподаватель экономики | 1 |
| прессовщик изделий из пластмасс с умением работать на литьевых машинах | 1 |
| прикладная геодезия | 1 |
| прикладная информатика в экономике | 1 |
| программист в компьютерных сетях | 1 |
| программное обеспечение вычислительных систем | 1 |
| продавец | 1 |
| проектирование радио | 1 |
| проектирование, эксплуатация, инжиниринг аэс | 1 |
| промешленное и гражданское строительство | 1 |
| промышленное и гражданское строительство | 1 |
| промышленный дизайн | 1 |
| психология бак + персонология маг | 1 |
| рабочий | 1 |
| радиомонтажник | 1 |
| радиотехника | 1 |
| редактор (газетно-журнальное издательское дело) | 1 |
| режиссёр | 1 |
| режиссура кино и телевидения | 1 |
| ремонт железнодорожного пути и путёвого хозяйство | 1 |
| русский язык и литература | 1 |
| рыбмастер | 1 |
| связь | 1 |
| секретарь | 1 |
| сервис и техническая эксплуатация транспортных и технологических машин. | 1 |
| сети связи | 1 |
| сис админ | 1 |
| системный администратор | 1 |
| системотехник | 1 |
| систему автоматического управления, но не защитил диплом | 1 |
| слесарь мср | 1 |
| слесарь по ремонту подвижного состава | 1 |
| соц.работа | 1 |
| социальная педагогика | 1 |
| социальная работа | 1 |
| социально-гуманитарное направление | 1 |
| социология | 1 |
| специалист по сервису | 1 |
| специалист по таможенному оформлению | 1 |
| специальности нет | 1 |
| среднее профессиональное образование | 1 |
| станочник широкого профиля | 1 |
| старший техник по и ас | 1 |
| стилист парикмахер | 1 |
| страховое дело | 1 |
| строитель | 1 |
| строитель кровельщик отделочник | 1 |
| строительство железных дорог | 1 |
| строительство и эксплуатация зданий и сооружений | 1 |
| тактико-специальная подготовка | 1 |
| таможенное дело | 1 |
| таможенное доле | 1 |
| телекоммуникации | 1 |
| техник - строитель | 1 |
| техник по информационным системам | 1 |
| техник программист | 1 |
| техник эвм | 1 |
| техник-коммерсант | 1 |
| техник-связи | 1 |
| техник-строитель | 1 |
| технолог молока и молочной продукции | 1 |
| технолог органических веществ | 1 |
| технолог поп | 1 |
| технология интернет | 1 |
| технология продуктов общественного питания | 1 |
| технология художественной обработки материалов | 1 |
| токарь | 1 |
| у меня нет специальности | 1 |
| управление персоналом | 1 |
| учет и аудит | 1 |
| училась на педагога по истории, но не окончила (незаконченное высшее образование - более трех лет) | 1 |
| учитель английского языка | 1 |
| учитель истории | 1 |
| учитель математики | 1 |
| учитель матнматики и физики | 1 |
| учитель начальных классов | 1 |
| учитель права | 1 |
| учитель технологии | 1 |
| учитель физической культуры и обж | 1 |
| учитель французского и немецкого языка | 1 |
| фармация | 1 |
| фельдшер | 1 |
| финансы и экономика | 1 |
| фрезеровщик | 1 |
| химик | 1 |
| художник | 1 |
| художник-мультипсикатор | 1 |
| школа | 1 |
| школьник | 1 |
| штукатур-маляр | 1 |
| экономика и бухгалтерский учёт | 1 |
| экономист-математик | 1 |
| экономическая безопасность | 1 |
| экспертиза и управление недвижимостью | 1 |
| электогазосварщик | 1 |
| электро монтёр связи | 1 |
| электрогазосварщик | 1 |
| электромеханик | 1 |
| электромонтёр | 1 |
| электроника и наноэлектроника | 1 |
| электронщик | 1 |
| электросварщик | 1 |
| электроснабжение | 1 |
| энергетик | 1 |
| юридическое | 1 |
| юрисконсульт | 1 |
| юриспруденөия | 1 |
taia %>%
mutate(spec2 = tolower(spec2)) %>%
mutate(spec2 = recode(spec2, "-2" = "NA")) %>%
group_by(spec2) %>%
summarise(n = n()) %>%
arrange(desc(n)) %>%
kable()## `summarise()` ungrouping output (override with `.groups` argument)
| spec2 | n |
|---|---|
| NA | 428 |
| бухгалтер | 4 |
| экономист | 3 |
| юрист | 3 |
| метролог | 2 |
| сварщик | 2 |
| design | 1 |
| автоматизация м связь | 1 |
| администратор салона красоты | 1 |
| архитектор | 1 |
| асу | 1 |
| библиотековед-библиограф | 1 |
| бухучет | 1 |
| водитель троллейбуса | 1 |
| гос управление | 1 |
| графический дизайн | 1 |
| дефектология | 1 |
| инженер виноделия | 1 |
| иностранный язык | 1 |
| культуролог | 1 |
| логопед | 1 |
| мастер отделочных и строительных работ | 1 |
| машинист крана | 1 |
| медбрат | 1 |
| медицинская сестра | 1 |
| менеджмент | 1 |
| менеджмент в сфере туризма | 1 |
| механик | 1 |
| наладчик холодильных установок | 1 |
| налогообложение | 1 |
| общий и стратегический менеджмент | 1 |
| оператор котельной | 1 |
| парикмахер-универсал | 1 |
| педагогика | 1 |
| переводчик | 1 |
| повар | 1 |
| правовед | 1 |
| преподаватель | 1 |
| программист | 1 |
| психология (неоконченное образование) | 1 |
| радиомонтажник | 1 |
| редактор | 1 |
| реставратор | 1 |
| сестринское дело | 1 |
| специалист | 1 |
| специалист аэропорта | 1 |
| специалист по охране труда | 1 |
| специалист по туристическим услугам | 1 |
| строительство и эксплуатация зданий и сооружений | 1 |
| телефонист | 1 |
| техник | 1 |
| тренер-преподаватель | 1 |
| управление персоналом | 1 |
| ученый-агроном | 1 |
| филология | 1 |
| финансовый менеджер | 1 |
| финансы | 1 |
| экономика | 1 |
| юриспруденция | 1 |
specs <- c(
"программист",
"информатик-экономист",
"информатика и вычислительная техника",
"оператор пк",
"программирование",
"бизнес-информатика",
"информатик- экономист",
"информатика и икт",
"информационная безопасность",
"информационные системы",
"информационные системы в экономике",
"информационные системы и технологии",
"информационные технологии и системы",
"кибернетика",
"компьютерная инженерия",
"компьютерные системы управления",
"прикладная информатика в экономике",
"программист в компьютерных сетях",
"программное обеспечение вычислительных систем",
"сис админ",
"системный администратор",
"техник по информационным системам",
"техник программист"
)taia %>%
mutate(spec1 = tolower(spec1),
spec2 = tolower(spec2),
dig_spec = ifelse(spec1 %in% specs | spec2 %in% specs, TRUE, FALSE)) -> taia##
## Welch Two Sample t-test
##
## data: taia$DT by taia$dig_spec
## t = -2.6551, df = 34.335, p-value = 0.01194
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -25.820608 -3.435371
## sample estimates:
## mean in group FALSE mean in group TRUE
## 152.9849 167.6129
taia %>%
mutate(jobfield = tolower(jobfield)) %>%
group_by(jobfield) %>%
summarise(n = n()) %>%
arrange(desc(n)) %>%
kable()## `summarise()` ungrouping output (override with `.groups` argument)
| jobfield | n |
|---|---|
| - | 156 |
| it | 18 |
| строительство | 16 |
| продажи | 14 |
| образование | 12 |
| торговля | 12 |
| не работаю | 9 |
| производство | 9 |
| транспорт | 8 |
| логистика | 7 |
| медицина | 6 |
| фриланс | 6 |
| охрана | 5 |
| самозанятый | 5 |
| финансы | 5 |
| жкх | 4 |
| информационные технологии | 4 |
| машиностроение | 4 |
| энергетика | 4 |
| _ | 3 |
| безопасность | 3 |
| графический дизайн | 3 |
| грузоперевозки | 3 |
| культура | 3 |
| металлургия | 3 |
| ржд | 3 |
| самозанятая | 3 |
| – | 2 |
| -1 | 2 |
| hr | 2 |
| банк | 2 |
| бухгалтерия | 2 |
| здравоохранение | 2 |
| ит | 2 |
| обслуживание | 2 |
| общепит | 2 |
| пенсионер | 2 |
| продажа | 2 |
| проектирование | 2 |
| реклама | 2 |
| социальная | 2 |
| спорт | 2 |
| телекоммуникации | 2 |
| туризм | 2 |
| фрилансер | 2 |
| юридическая сфера | 2 |
| ____ | 1 |
| — | 1 |
| —– | 1 |
| —————- | 1 |
| — | 1 |
| 3-печать | 1 |
| it (1c) | 1 |
| it-сфера | 1 |
| it-технологии | 1 |
| it, коммуникации | 1 |
| orm/smm | 1 |
| smm | 1 |
| автомобильный бизнес | 1 |
| адвокат | 1 |
| атб | 1 |
| банковское дело | 1 |
| безопасность персонала | 1 |
| бизнес, торговля | 1 |
| биология, медицина | 1 |
| бухгалтер | 1 |
| бухгалтерский учет | 1 |
| в интернете | 1 |
| визажист | 1 |
| геодезия | 1 |
| госструктура | 1 |
| гостинечный бизнес | 1 |
| гостиничный бизнес | 1 |
| государственная служба | 1 |
| дизайн | 1 |
| дизайн интерьеров | 1 |
| добыча полезных ископаемых | 1 |
| иллюстрация | 1 |
| интернет | 1 |
| информатика | 1 |
| информационная безопасность | 1 |
| информационные технологии в медицине | 1 |
| ип | 1 |
| ип в сфере туризма | 1 |
| искусство | 1 |
| искусство и культура | 1 |
| кинопроизводство | 1 |
| колл центр | 1 |
| коммунальные услуги | 1 |
| консалтинг | 1 |
| литература, театр. | 1 |
| логистика складская | 1 |
| маркетинг | 1 |
| мебель | 1 |
| медиа | 1 |
| музыка | 1 |
| музыкальное образование | 1 |
| музыкант | 1 |
| мфц | 1 |
| мчс россии | 1 |
| налоговая сфера | 1 |
| научно-исследовательская деятельность, ветеринария, эпидемиология | 1 |
| недвижимость | 1 |
| нефте добыча | 1 |
| нефтяная | 1 |
| нефтянка | 1 |
| обслуживание и ремонт контрольно-кассовой техники | 1 |
| обслуживание населения | 1 |
| оказание услуг | 1 |
| онлайн-обучение | 1 |
| оптовая торговля | 1 |
| остин | 1 |
| охрана труда | 1 |
| охранная деятельность | 1 |
| пенсионерка | 1 |
| пивной блогер | 1 |
| пномышленность | 1 |
| право | 1 |
| программист | 1 |
| продажа кухонной мебели, дизайн | 1 |
| продажа мебели | 1 |
| производство мебели | 1 |
| производство металлоконструкций | 1 |
| производство продуктов питания | 1 |
| производство систем охлаждения | 1 |
| производство х/булочных изделий | 1 |
| промышленное производство | 1 |
| рабочий на заводе | 1 |
| развлечения | 1 |
| разметка информации | 1 |
| ремонт бытовой техники | 1 |
| ремонт электроники | 1 |
| ресторанный бизнес | 1 |
| рыболовство | 1 |
| самозанятый it специалист | 1 |
| связь | 1 |
| связь и телекоммуникации | 1 |
| сельское хозяйство и производство | 1 |
| склад | 1 |
| служба доставки | 1 |
| соц защита | 1 |
| социалка | 1 |
| стоматология | 1 |
| страхование | 1 |
| строителство | 1 |
| строительство и дизайн | 1 |
| строительство, ремонт | 1 |
| сфера жкх | 1 |
| сфера обслуживания | 1 |
| сфера розничных продаж | 1 |
| сфера услуг | 1 |
| такси | 1 |
| телекоммуникация | 1 |
| телекоммуникация. | 1 |
| телемуникация | 1 |
| тестирование | 1 |
| техника | 1 |
| технологии | 1 |
| типография | 1 |
| транпортные перевозки | 1 |
| умные охранные системы | 1 |
| университет | 1 |
| управление персоналом | 1 |
| услуги населению, бюджетная организация. | 1 |
| фармация | 1 |
| флористика | 1 |
| частный предприниматель | 1 |
| экологическое проектирование | 1 |
| экономика | 1 |
| эконофика | 1 |
| экстренная служба | 1 |
| электронная подпись | 1 |
| юзабилити | 1 |
| юриспруденция | 1 |
| я работаю в сфере it | 1 |
jobs <- c(
"it",
"информационные технологии",
"ит",
"it (1c)",
"it-сфера",
"it-технологии",
"it, коммуникации",
"информатика",
"информационная безопасность",
"информационные технологии в медицине",
"программист",
"самозанятый it специалист",
"я работаю в сфере it",
"юзабилити",
"техника",
"технологии"
)taia %>%
mutate(jobfield = tolower(jobfield),
dig_job = ifelse(jobfield %in% jobs, TRUE, FALSE)) -> taia##
## Welch Two Sample t-test
##
## data: taia$DT by taia$dig_job
## t = -0.62167, df = 40.728, p-value = 0.5376
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -15.225044 8.058987
## sample estimates:
## mean in group FALSE mean in group TRUE
## 153.6332 157.2162